Stability of the Southern Ocean Oscillation is Uncertain. 2022-06-27. Jorma Jyrkkanen

SpringerLink Open Access Published: 11 February 2021 Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models Goratz Beobide-Arsuaga, Tobias Bayr, Annika Reintges & Mojib Latif Climate Dynamics volume 56, pages 3875–3888 (2021)Cite this article 3431 Accesses 14 Citations 19 Altmetric Metrics details Abstract There is a long-standing debate on how the El Niño/Southern Oscillation (ENSO) amplitude may change during the twenty-first century in response to global warming. Here we identify the sources of uncertainty in the ENSO amplitude projections in models participating in the Coupled Model Intercomparison Phase 5 (CMIP5) and Phase 6 (CMIP6), and quantify scenario uncertainty, model uncertainty and uncertainty due to internal variability. The model projections exhibit a large spread, ranging from increasing standard deviation of up to 0.6 °C to diminishing standard deviation of up to − 0.4 °C by the end of the twenty-first century. The ensemble-mean ENSO amplitude change is close to zero. Internal variability is the main contributor to the uncertainty during the first three decades; model uncertainty dominates thereafter, while scenario uncertainty is relatively small throughout the twenty-first century. The total uncertainty increases from CMIP5 to CMIP6: while model uncertainty is reduced, scenario uncertainty is considerably increased. The models with “realistic” ENSO dynamics have been analyzed separately and categorized into models with too small, moderate and too large ENSO amplitude in comparison to instrumental observations. The smallest uncertainties are observed in the sub-ensemble exhibiting realistic ENSO dynamics and moderate ENSO amplitude. However, the global warming signal in ENSO-amplitude change is undetectable in all sub-ensembles. The zonal wind-SST feedback is identified as an important factor determining ENSO amplitude change: global warming signal in ENSO amplitude and zonal wind-SST feedback strength are highly correlated across the CMIP5 and CMIP6 models. 3879Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models1 3 taken over the period 1979–2005 to the projected long-term trend X fp: Which polynomial fit should be used to represent the long-term trend, and hence, the response to the (4)xf (s, m, t) = Xfp(s, m, t) − i(s, m) anthropogenic forcing? On the one hand, choosing a too high order of the polynomial fit would artificially decrease the level of internal variability. On the other hand, the order of the fit must be high enough to adequately describe the nonlinear externally forced trend. Figure 3 depicts the polynomial fits of the 2nd, 3rd, 4th and 5th order cal- culated from the GFDL-ESM2M model. Under strong 0.5 1 1.5 Wind stress feedback (Pa/K) -25 -20 -15 -10 -5 0 Heat flux feedback (W/m² per K) 1 2 3 4 5 6 78 9 10 11 12 13 14 15 16 17 18 1920 2122 23 24 25 26 27 28 29 3031 32 3334 35 3637 38 39 40 41 42 43 44 45 46 4748 49 50 5152 53 54 55 56 Cor: -0.55 x10 -2 (a) ERA Interim ERA40 0 0.5 1 Normalized mean atmospheric feedbacks -2.5 -2 -1.5 -1 -0.5 0 0.5 Rel. Nino4 SST bias (°C) 1 2 3 4 5 6 78 9 10 11 12 13 14 1516 17 18 1920 2122 2324 25 26 27 28 29 303132 33 34 3536 37 38 3940 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 Cor: 0.63 (b) ERA Interim ERA40 0 0.5 1 Normalized mean atmospheric feedbacks -0.5 0 0.5 1 1.5 ENSO non-linearity 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 2324 25 26 27 28 29 303132 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 Cor: 0.71 (c) ERA Interim ERA40 Fig. 1 For individual CMIP5 and CMIP6 models and reanalysis prod- ucts: a net heat flux feedback, defined as regression of net heat flux in Niño3 and Niño4 on SST in Niño3.4 on the y-axis, vs. the zonal wind stress feedback, defined as regression of zonal wind stress in Niño4 region on SST in Niño3.4 region on the x-axis; b atmospheric feedback strength (average of wind stress and heat flux feedback, after normalizing each by the average reanalysis value) on x-axis vs. rela- tive SST bias in the Niño4 region (model output SST minus observed SST, after subtracting the tropical Pacific area mean SST from each); c atmospheric feedback strength (average of wind stress and heat flux feedback, after normalizing each by the average reanalysis value) on x-axis vs. ENSO non-linearity, computed as the difference between Niño3 and Niño4 SSTA skewness; crosses indicate CMIP5 models and triangles CMIP6 models; the red colored symbols indicate mod- els with strong atmospheric feedbacks and realistic ENSO dynam- ics, called “Strong” sub-ensemble; the blue colored symbols indicate models with weak atmospheric feedbacks, called “Weak” sub-ensem- ble; the correlation with a 95% confidence level is shown 1900 1920 1940 1960 1980 2000 0.4 0.5 0.6 0.7 0.8 0.9 1 ENSO amplitude (°C) 10-year window(a) 1900 1920 1940 1960 1980 2000 0.4 0.5 0.6 0.7 0.8 0.9 1 ENSO amplitude (°C) 20-year window(b) 1900 1920 1940 1960 1980 2000 0.4 0.5 0.6 0.7 0.8 0.9 1 ENSO amplitude (°C) 30-year window(c) Fig. 2 ENSO amplitude defined as the running standard deviation of Niño3.4 SSTA in HadISST obtained with: a 10-year, b 20-year and c 30-year window 3880 G. Beobide-Arsuaga et al.1 3 external forcing (RCP8.5), the different orders yield a similar pattern. However, under weak forcing (RCP4.5), only the 2nd order seems to adequately capture the forced trend. Therefore, the 2nd order fit has been chosen. We note in this context that ENSO amplitude can vary inter- nally on multidecadal and centennial time scales (Li et al. 2013). The key results of the uncertainty analysis, how- ever, remain very similar if a higher-order polynomial fit is used. Using the long-term signal anomaly x f (4), we can com- pute the spread between the model projections and then aver- age it over the three scenarios. This will be our inter-model uncertainty that is time-dependent (5). Next, by averaging x f over all models for each scenario and computing the spread within the two of them, we get the scenario uncertainty that also is time-dependent (6). Last, by computing the spread of each model’s internal variability over time, and then averaging over all models and scenarios, we obtain the internal variability uncertainty, which is independent of time (7). The time evolution of the internal variability has been estimated, with the conclusion that it does not show any relevant differences between different periods (Fig. 4). To test whether the global warming signal in ENSO amplitude is statistically significant, we use the signal-to- noise ratio SNR (8). The average of x f over all models and scenarios corresponds to the signal, G (9), and the noise to the 95 th percentile of the standard normal distribution q c/2 (5)M(t) = 1 Ns ⋅  s std m xf (s, m, t) (6)S(t) = std s  1 Nm ⋅  m xf (s, m, t)  (7)I = 1 Ns ⋅  s 1 Nm ⋅  m std t(𝜖(s, m, t)) multiplied by total uncertainty T (10). If the ratio is greater than unity the climate signal is considered detectable. Finally, in order to identify possible origins of the uncer- tainties, two factors have been considered: the projected mean zonal SST gradient and wind-SST feedback. The mean zonal SST gradient is defined as the temperature difference between the Niño4 and Niño1 + 2 (90° W–80° W, 0°–10° (8)SNR(t) = G(t) q c 2 ⋅ T(t) (9)G(t) = 1 Ns ⋅  s 1 Nm ⋅  m xf (s, m, t) (10)T(t) = M(t) + S(t) + I Fig. 3 Historical and pro- jected ENSO amplitude for the GFDL-ESM2M model (black) with 2nd (blue), 3rd (red), 4th (green) and 5th (cyan) order polynomial fits: in a for RCP4.5 and in b RCP8.5 scenario RCP 4.5 RCP 8.5 1900 1950 2000 2050 2100 1 1.2 1.4 1.6 ENSO Amplitude (°C) ENSO AMPLITUDE RCP4.5 GFDL-ESM2M(a) 1900 1950 2000 2050 2100 1 1.2 1.4 1.6 ENSO Amplitude (°C) ENSO AMPLITUDE RCP8.5 GFDL-ESM2M(b) 2010 2020 2030 2040 2050 2060 2070 2080 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 ENSO Amplitude (°C) INTERNAL VARIABILITY RCP4.5/SSP245 RCP8.5/SSP585 Fig. 4 Internal long-term ENSO amplitude variability for each model and scenario, defined as the difference between the ENSO amplitude and the polynomial fit: green for RCP4.5/SSP2-4.5 scenario and red for RCP8.5 /SSP5-8.5 scenario; thin dashed lines correspond to indi- vidual model simulations and thick solid lines to the scenario means; black solid line is the mean over all simulations 3881Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models1 3 S) regions relative to the tropical Pacific (120° E–80° W, 15° N–15° S) averaged temperatures. The wind-SST feed- back is defined as described above. A 30-year low-pass filter has been applied to the zonal SST gradient and the wind feedback has been computed relative to a 30-year running window. The same methodology as that used for the ENSO amplitude is applied to obtain the global warming signal of the zonal SST gradient and wind-SST feedback. Then, we relate the inter-model spread of the projected ENSO ampli- tude change to the two factors at the end of the twenty-first century. 3 ENSO amplitude and decadal variability First, we investigate the ENSO amplitude and its decadal variability in the preindustrial control simulations. The Niño3.4 SSTA is homogenized to the length of 240 years. We have computed the standard deviation of the decadal ENSO amplitude and plotted it against the standard devia- tion of the interannual Niño3.4 SSTA (Fig. 5). There is a positive relationship with a correlation coefficient of 0.64: models with large (small) interannual Niño3.4 SSTA vari- ability tend to show strong (weak) decadal ENSO ampli- tude variations. Further, there is a large spread between the models, from much lower to much higher interannual and decadal variability relative to observations. We divide the models into 3 main sub-ensembles: (1) models with high interannual and decadal ENSO variability (“High” sub- ensemble, red squares and triangles), (2) models with low interannual and decadal ENSO variability (“Low” sub- ensemble, blue squares and triangles), and (3) models with the closest variability to observations, moderate interannual and decadal ENSO variability (“Moderate” sub-ensemble, green squares and triangles). Although there is a quite strong linear relationship between interannual and decadal ENSO variability, we consider both the interannual and the dec- adal ENSO variability for defining the sub-ensembles. If we would only use interannual ENSO variability, we would mix models with different decadal ENSO variability: for instance, we would add several models with unrealistically low decadal ENSO variability as “Moderate”. 4 Global warming signal of the ENSO amplitude and its uncertainties Figure 6 depicts the global warming signal of the ENSO amplitude. Dashed thin lines in Fig. 6a) represent the indi- vidual model simulations for RCP4.5/SSP2-4.5 and RCP8.5/ SSP5-8.5 in green and red, respectively. The thick green and red solid lines correspond to the scenario averages and the black solid line to the total scenario and model mean. The strong model disagreement in ENSO amplitude change towards the end of the twenty-first century is clearly visible. The strongest forcing scenario, RCP8.5/SSP5-8.5, contains most of the positive ENSO amplitude changes, while the RCP4.5/SSP2-4.5 simulations are equally balanced between the positive and negative change of the amplitude. The sce- nario average shows a positive global warming signal for the strongest forcing case and a signal close to zero for RCP4.5/SSP2-4.5 scenario. The total scenario and model mean (thick black line) lays between the two scenario means, showing a slight increase of the ENSO amplitude. In Fig. 6b), we divide the global warming signal aver- ages for the end of the twenty-first century (RCP4.5/ SSP2-4.5 green, RCP8.5/SSP5-8.5 red) into models with high interannual and decadal ENSO variability, moder- ate interannual and decadal ENSO variability and low interannual and decadal ENSO variability. Vast climate sensitivity differences are present between CMIP models, which has been incremented for the latest phase, CMIP6 (Andrews et al. 2012; Meehl et al. 2020). Prior the sub- ensemble mean, each model’s ENSO amplitude change is divided by the global mean temperature difference between 2050–2099 and 1920–1970 under RCP8.5/SSP5- 8.5. Under RCP4.5/SSP2-4.5, CMIP5’s and CMIP6’s forced signal in all three sub-ensembles is close to zero. Under RCP8.5/SSP5-8.5, there are noticeable differences. While CMIP5 models with high decadal ENSO variability project by the end of the twenty-first century a decrease in ENSO amplitude, the “Moderate” and “Low” sub-ensem- bles show an increase in ENSO amplitude. However, the 0.5 1 1.5 Nino3.4 SSTA STD (°C) 0.05 0.1 0.15 0.2 Decadal Amplitude STD (°C) 1 2 3 4 5 6 78 9 10 11 12 13 14 15 16 17 18 19 20 2122 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 Cor: 0.64 HadISST ERSST Fig. 5 Pre-industrial interannual Niño3.4 SSTA standard deviation on the x-axis vs. decadal Niño3.4 SSTA standard deviation on the y-axis. Models are grouped into “High” (red), “Moderate” (green) and “Low” (blue) interannual and decadal ENSO variability models; crosses indicate CMIP5 models and triangles CMIP6 models; Had- ISST and ERSST data sets are shown in magenta and cyan, respec- tively; the correlation with a 95% confidence level is shown 3882 G. Beobide-Arsuaga et al.1 3 error bars, representing the maximum and minimum val- ues, show a large spread between approximately ± 0.1. The three sub-ensembles of CMIP6 models on the other hand agree on the increase of ENSO amplitude. The strongest mean ENSO amplitude change is for “High” sub-ensem- ble. The error bars show a wide range of positive ENSO amplitude changes. It is important to note that we show the result for 20 CMIP6 models, while CMIP5 contains 36 models. At the time of our research, the output of 20 CMIP6 models was available for the variables and sce- narios we use. Considering the large spread shown by CMIP5 models, it is possible that 20 models do not rep- resent the full inter-model spread of CMIP6 models. The combination of CMIP5 and CMIP6 leads to an average positive ENSO amplitude change for “High”, “Moderate” and “Low” sub-ensembles. When only considering the models with strong ENSO atmospheric feedbacks (Fig. 6c, d), the strongest positive and negative ENSO amplitude changes are reduced, shifting the RCP4.5/SSP2-4.5 scenario and the total means slightly towards negative values (Fig. 6c). The RCP4.5/SSP2-4.5 scenario generally projects a decrease of the mean ENSO amplitude over the sub-ensembles (Fig. 6d). In the RCP8.5 scenario, the CMIP5 “High” sub-ensemble shows a stronger decrease of the ENSO amplitude than in RCP4.5. In contrast, CMIP6 models disagree on the sign of the ENSO ampli- tude change between the two scenarios. The combination of CMIP5 and CMIP6 sub-ensembles are not able to show any consistent result of global warming signal of ENSO amplitude. Looking into models with weak ENSO atmospheric feedbacks (Fig. 6e, f), both scenario means and total mean point towards an increase of ENSO amplitude (Fig. 6e). The strongest projected ENSO amplitude change is shown by the RCP8.5/SSP5-8.5 scenario: all sub-ensembles agree on the increase of ENSO amplitude under the strongest forcing scenario for both CMIP5 and CMIP6 ensembles (Fig. 6f). In addition, when comparing to strong ENSO atmospheric Fig. 6 Global warming signal of the ENSO amplitude calculated by subtracting the historical long-term trend (1979–2005) to the projected long-term trend (2005–2099) in a, c, e and to the end of the projected long-term trend (2099) in b, d, f; in a individual simulations (dashed lines), RCP4.5/SSP2- 4.5 scenario mean (solid green line), RCP8.5/SSP5-8.5 sce- nario mean (solid red line) and mean over all simulations (solid black line); in b) mean over “High”, “Moderate” and “Low” sub-ensembles, for RCP4.5/ SSP2-4.5 (green) and RCP8.5/ SSP5-8.5 (red) scenarios after dividing each model by its climate sensitivity, computed as the global mean temperature difference between 2050–2099 and 1920–1970 under RCP8.5/ SSP5-8.5 scenario; error bars show the maximum and minimum value for each sub- ensemble; in c, d same as a, b, but here for the “Strong” sub-ensemble; in e, f same as a, b, but here for the “Weak” sub-ensemble All Models 2010 2020 2030 2040 2050 2060 2070 2080 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 ENSO Amplitude Change (°C) GLOBAL WARMING SIGNAL(a) RCP4.5/SSP245 RCP8.5/SSP585 GLOBAL WARMING SIGNAL CMIP5 CMIP6 CMIP5+6 (b) High Moderate Low High Moderate Low High Moderate Low-0.2 -0.1 0 0.1 0.2 ENSO amplitude change Strong sub-ensemble 2010 2020 2030 2040 2050 2060 2070 2080 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 ENSO Amplitude Change (°C) GLOBAL WARMING SIGNAL(c) RCP4.5/SSP245 RCP8.5/SSP585 GLOBAL WARMING SIGNAL CMIP5 CMIP6 CMIP5+6 (d) High Moderate Low High Moderate High Moderate Low-0.2 -0.1 0 0.1 0.2 ENSO amplitude change Weak sub-ensemble 2010 2020 2030 2040 2050 2060 2070 2080 -0.6 -0.4 -0.2 0 0.2 0.4 0.6 ENSO Amplitude Change (°C) GLOBAL WARMING SIGNAL(e) RCP4.5/SSP245 RCP8.5/SSP585 GLOBAL WARMING SIGNAL CMIP5 CMIP6 CMIP5+6 (f) High Moderate Low High Moderate Low High Moderate Low-0.2 -0.1 0 0.1 0.2 ENSO amplitude change 3883Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models1 3 feedback models, the positive ENSO amplitude change is stronger in all sub-ensembles with weak ENSO atmospheric feedbacks except for “Moderate” in CMIP5. We next quantify and identify the main sources of uncer- tainty in the projections (Fig. 7). The total uncertainty increases towards the end of the twenty-first century from 0.11 °C to approximately 0.35 °C. In the first three decades, the most important source of uncertainty is the internal dec- adal variability (green). The internal variability uncertainty amounts to approximately 0.07 °C, corresponding to around 65% of the total uncertainty at the beginning of the pro- jection. After 2034, the main uncertainty source is model uncertainty (blue). It exceeds 0.21 °C by 2100, which corre- sponds to roughly 60% of the total uncertainty. The scenario uncertainty (red) is of similar magnitude as the internal- variability uncertainty at the end of the twenty-first century. However, it is the smallest uncertainty source at all times. We note that the scenario uncertainty is the largest contri- bution to the total uncertainty by 2100 when analyzing pro- jections of globally averaged surface temperature, doubling the surface temperature warming from RCP4.5 to RCP8.5, and from SSP2-4.5 to SSP5-8.5 (Knutti and Sedláček 2013; Gidden et al. 2019). We repeat the uncertainty analysis for CMIP5, CMIP6 and all sub-ensembles, which have been defined above: “Strong”, “Weak”, “High”, “Low” and “Moderate”. We also use the combined selection of “Strong” with “Moder- ate” sub-ensembles, as the models of this sub-ensemble are closest to observed ENSO in terms of amplitude variabil- ity and atmospheric feedback strength. In Fig. 8a) we show the results of the uncertainty analysis, and in Fig. 8b) the signal-to-noise ratio, both towards the end of the twenty- first century. Model uncertainty is the largest contributor to the total uncertainty in all sub-ensembles. This result again stresses the importance of the model uncertainty in global warming projections of ENSO amplitude. From CMIP5 to CMIP6 the model uncertainty is reduced, while the scenario uncertainty is largely increased leading to an increase of total uncertainty. The smallest total and model uncertainties Fig. 7 a ENSO amplitude uncertainty divided into model (blue), internal variability (green) and scenario uncertainty (red); in b relative uncertainties; solid vertical line represents where model uncertainty becomes larger than internal variability uncertainty ENSO AMPLITUDE UNCERTAINTY(a) 2010 2020 2030 2040 2050 2060 2070 2080 0 0.1 0.2 0.3 Uncertainty (°C) Model unc. Internal unc. Scenario unc. RELATIVE UNCERTAINTY(b) 2010 2020 2030 2040 2050 2060 2070 2080 10 30 50 70 90 Relative Uncertainty (%) Model unc. Internal unc. Scenario unc. SUB-ENSEMBLE UNCERTAINTIES(a) All CMIP5 CMIP6 Strong Weak High Low Moderate Strong + Moderate 0 0.2 0.4 0.6 Uncertainty (°C) Total Model Internal Scenario SUB-ENSEMBLE SIGNAL-NOISE RATIO(b) All CMIP5 CMIP6 Strong Weak High Low Moderate Strong + Moderate 0 0.05 0.1 0.15 Fig. 8 a Total (black), model (blue), internal variability (green) and scenario (red) uncertainties at the end of the projection, year 2099, and b signal-to-noise ratio for: all models, CMIP5 models, CMIP6 models, “Strong”, “Weak”, “High”, “Low” and “Moderate” sub- ensembles, and the combination of “Strong” and “Moderate” sub- ensembles 3884 G. Beobide-Arsuaga et al.1 3 are observed when combining “Strong” and “Moderate” sub-ensembles. However, even restricting the models to this sub-ensemble does lower the total uncertainty only by 0.045 °C (13%) and model uncertainty by 0.05 °C (24%) in comparison to considering all models (0.35 °C and 0.21 °C, respectively). Further, the signal-to-noise ratio (Fig. 8b) does not exceed the value of unity for any sub-ensemble, which means that a global warming signal in ENSO amplitude can- not be detected with high statistical significance. We depict the change in ENSO amplitude by the end of the twenty-first century for “High”, “Moderate” and “Low” for all models in Fig. 9 and for “Strong” in Fig. 10. In Fig. 9 the mod- els within each sub-ensemble largely disagree. Although the projected ENSO amplitude changes in “Strong” are reduced, there is no consistency within the sub-ensembles (Fig. 10). Under the strongest scenario, models in “High” (left group in Fig. 10) agree on a reduced ENSO amplitude for CMIP5 (models 7–36), while for CMIP6 models show an increase of ENSO amplitude (41–56). On the other hand, five out six models in “Moderate” (central group in Fig. 10) point towards an increase under RCP8.5/SSP5-8.5, except for the NorESM1- M model (number 35). In “Low”, there only are three models and it is hard to derive a conclusion. In summary, although models with the most realistic ENSO dynamics and with clos- est ENSO amplitudes to observations generally point towards an increase of ENSO amplitude, the global warming signal is still robustly undetectable due to the large inter-model disagreements. 5 ENSO amplitude inter‑model uncertainty source Several studies have shown that ENSO amplitude is strongly influenced by the background mean state (Knutson et al. 1997; McPhaden et al. 2011; Hu et al. 2013; Kim et al. 2014a) and the wind-SST feedback (Lloyd et al. 2009; Vijayeta and Dom- menget 2018). The background mean state has an influence on ENSO amplitude via the strength of the surface–subsurface coupling (Hu et al. 2013). Changes on climatological trade winds, which affect zonal SST gradient, vary the response of the zonal thermocline slope to zonal wind anomalies (Kim et al. 2014a). In the framework of the recharge oscillator model, Vijayeta and Dommenget (2018) could show under present day condition that the wind-SST feedback has the strongest influence on ENSO amplitude. Although ENSO is a complex phenomenon, we only focus in the following on these two factors to get insight into origin of the inter-model spread. In Fig. 11a, b), we show the global warming signal of the ENSO amplitude and the wind-SST feedback. A strong positive linear relationship is detected with corre- lation coefficients of 0.90 (RCP4.5/SSP2-4.5, Fig. 11a) and 0.84 (RCP8.5/SSP5-8.5, Fig. 11b). In “Strong” (red color), the correlation coefficients amount to 0.95 and 0.91, respectively. The relationship between the projected ENSO amplitude change and the zonal SST gradient is not as strong. While model ensemble exhibits a large spread of ENSO amplitude change, most of them project a decrease of the zonal SST gradient (Fig. 11c, d). The correlation coefficients are for -0.36 (RCP4.5/SSP2-4.5) and -0.25 (RCP8.5/SSP5-8.5). The “Strong” sub-ensemble models show an improved correlation of -0.58 and -0.45. When calculating the SST gradient with different box averages, the results are virtu- ally unchanged. Therefore, we conclude that the change in ENSO Amplitude Change High Moderate Low 4 5 7 8 15 16 18 19 29 36 39 40 41 42 43 44 46 49 50 52 55 56 1 2 6 9 11 12 13 14 17 20 24 26 28 32 35 37 45 51 53 54 3 10 21 22 23 25 27 30 31 33 34 38 47 48 -0.2 -0.1 0 0.1 0.2 ENSO amplitude change RCP4.5/SSP2-4.5 RCP8.5/SSP5-8.5 Fig. 9 ENSO amplitude change between 2005 and 2099, computed as a change of the long-term trend, divided by the climate sensitivity of each model, computed as the global mean temperature difference between 2050–2099 and 1920–1970 under RCP8.5/SSP5-8.5 sce- nario; vertical dashed lines divide from the left to the right; “High”, “Moderate” and “Low” ENSO amplitude sub-ensembles, respectively ENSO Amplitude Change High Moderate Low 7 8 18 29 36 41 42 43 50 56 9 11 12 28 35 53 10 21 22 -0.2 -0.1 0 0.1 0.2 ENSO amplitude change RCP4.5/SSP2-4.5 RCP8.5/SSP5-8.5 Fig. 10 Same as Fig. 9, but here for the “Strong” sub-ensemble 3885Uncertainty of ENSO-amplitude projections in CMIP5 and CMIP6 models1 3 wind-SST feedback is an important factor of ENSO ampli- tude under global warming. 6 Summary and discussion Using a CMIP5 and CMIP6 multi-model ensemble, the global warming signal in projected ENSO amplitude and the corresponding uncertainties have been quantified. The uncertainties have been split into the model uncertainty (spread of ENSO amplitude change within the ensemble), scenario uncertainty (spread of ENSO amplitude change caused by the different scenarios), and internal variabil- ity uncertainty (spread due to decadal ENSO variability). CMIP5 and CMIP6 models highly disagree with respect to future ENSO amplitude change. Projected changes range from decreasing to increasing ENSO amplitude (from − 0.4 to + 0.6 °C), with the mean global warming signal averaged over all models and scenarios close to zero. Many state-of-the-art coupled climate models fail to simulate realistic ENSO characteristics. Therefore, models with realistic ENSO feedbacks and thus possibly realistic ENSO dynamics have been identified and grouped into the “Strong” sub-ensemble. The “Strong” sub-ensemble con- tains the models that are able to simulate the non-linearity of ENSO most realistically (Cai et al. 2020; Hayashi et al. 2020). We also have investigated the unforced decadal vari- ability of the ENSO amplitude. From this latter analysis, three additional sub-ensembles have been formed: models with high and low interannual and decadal ENSO variability, termed “High” and “Low”, respectively, and models with moderate interannual and decadal ENSO variability, termed “Moderate”. The later sub-ensemble is the closest to the observed ENSO variability. Within CMIP5 models, the “High” sub-ensemble pro- jects a reduction of the ENSO amplitude towards the end of the twenty-first century, while “Moderate” and “Low” sub-ensembles indicate an increase. When only considering realistic ENSO dynamic models, the “Strong” sub-ensemble, the signal is intensified: the negative and positive changes of the ENSO amplitude are increased both for “High” and “Moderate”, respectively. The result is consistent between scenarios: the signal is stronger for the RCP8.5 scenario than for the RCP4.5. In contrast, most of CMIP6 models under SSP5-8.5 scenario project an increase in ENSO amplitude towards the end of the twenty-first century, in agreement with recent studies (Fredriksen et al. 2020). The strongest increase is projected by models with high interannual and decadal ENSO variability. When considering the “Strong” sub-ensemble, the positive signal of ENSO amplitude Fig. 11 Inter-model relationship between the global warming signal of the ENSO amplitude change (x-axis) and; a, b the zonal wind stress-SST feedback change; c, d the Pacific equato- rial mean zonal SST gradi- ent change (y-axis) for; a, c RCP4.5/SSP2-4.5 scenario, and b, d RCP8.5/SSP5-8.5 scenario; crosses indicate CMIP5 models and triangles CMIP6 models; red corresponds to “Strong” sub-ensemble; the correlation with a 95% confidence level is shown RCP4.5/SSP2-4.5 RCP8.5/SSP5-8.5 -0.4 -0.2 0 0.2 0.4 0.6 ENSO Amplitude Change (°C) -6 -4 -2 0 2 4 6 Wind Feedback Change (Pa/°C) 10 -3 GLOBAL WARMING ANOMALY(a) Cor. coef: 0.90 0.95 -0.4 -0.2 0 0.2 0.4 0.6 ENSO Amplitude Change (°C) -6 -4 -2 0 2 4 6 Wind Feedback Change (Pa/°C) 10 -3 GLOBAL WARMING ANOMALY(b) Cor. coef: 0.84 0.91 -0.4 -0.2 0 0.2 0.4 0.6 ENSO Amplitude Change (°C) -1.5 -1 -0.5 0 0.5 1 1.5 SST Gradient Change (°C) GLOBAL WARMING ANOMALY(c) Cor. coef: -0.36 -0.58 -0.4 -0.2 0 0.2 0.4 0.6 ENSO Amplitude Change (°C) -1.5 -1 -0.5 0 0.5 1 1.5 SST Gradient Change (°C) GLOBAL WARMING ANOMALY(d) Cor. coef: -0.25 -0.45 3886 G. Beobide-Arsuaga et al.1 3 change is reduced. In this case, the result is not consist- ent between the scenarios: models under SSP2-4.5 scenario project a decrease of the ENSO amplitude. At this point, we must keep in mind that in this study we have been able to use 20 CMIP6 models in comparison to 36 CMIP5 models. Looking into models with weak ENSO atmospheric feed- backs, all sub-ensembles besides “Moderate” in CMIP5 show a stronger positive ENSO amplitude change than “Strong” models. In conclusion, the global warming sig- nal of ENSO amplitude highly varies between CMIP5 and CMIP6, and the studied sub-ensembles. The total uncertainty in the projected ENSO amplitude change obtained from all CMIP5 and CMIP6 models exhib- its an increase over time: 0.11 °C at the beginning to 0.35 °C towards the end of the twenty-first century. Internal vari- ability is the main contributor to the total uncertainty during the first three decades. The inter-model differences domi- nate thereafter, while scenario uncertainty is relatively small throughout the entire twenty-first century. CMIP6 models show a larger uncertainty than CMIP5 models. Although the model uncertainty is decreased, the scenario uncertainty is considerably increased (from 0.04 to 0.12 °C). This is in general agreement with previous studies indicating a greater climate sensitivity for CMIP6 models (Meehl et al. 2020). The largest uncertainty within a sub-ensemble is observed in “High”, approximating to 0.4 °C, and the smallest uncer- tainty when combining “Strong” and “Moderate” (about 0.3 °C). However, as shown by the signal-to-noise ratio, the global warming signal in the projected ENSO amplitude change is too small to be robustly detectable. Finally, we have investigated two potential sources for the strong inter-model differences. The model spread is highly correlated with the spread in wind-SST feedback change, with a correlation coefficient of 0.90 and 0.84 for RCP4.5/ SSP2-4.5 and RCP8.5/SSP5-8.5 scenarios, respectively. This suggests that it is important to understand the factors determining the wind-SST feedback under global warm- ing to reduce uncertainty in ENSO-amplitude projections. However, from our analysis one cannot assure that the wind feedback is the dominant contributor to the future ENSO amplitude change, as it might partially be canceled by the change of the thermodynamic negative feedback, e.g., the shortwave feedback. A quantitative comparison between the positive and the negative feedback in terms of the ENSO amplitude change is out of scope of this paper. The correla- tion with the change in mean zonal SST gradient is of − 0.36 and − 0.25. While most of the models agree on the reduc- tion of the mean zonal SST gradient under global warm- ing, the response of the wind feedback is extremely model dependent. This discrepancy between the mean state changes and the wind feedback changes is a puzzling question that needs to be answered in the future. A previous study has shown that there is a non-linear relation between mean-state changes and ENSO amplitude, in which ENSO amplitude increases till an optimum and then decreases again (Hu et al. 2013). Considering the large mean state biases present in climate models, this might explain why the ENSO amplitude change varies to a similar mean state changes. In fact, if we consider realistic ENSO dynamic models, which show the smallest Niño4 SST bias, the inter-model correlation with SST gradient change is increased to − 0.58. In addition, the wind-SST feedback strength is strongly linked to the ris- ing branch of the Walker Circulation, which again highly depends on the mean state (Bayr et al. 2020). Similarly, there is an ongoing debate about how the Walker Circulation will change under global warming (Knutson et al. 1997; Vecchi and Soden 2007; DiNezio et al. 2009, 2013; Sohn and Park 2010; Yu and Zwiers 2010; Power and Kociuba 2010, 2011; Meng et al. 2012; Luo et al. 2012; L’Heureux et al. 2013; Bayr et al. 2014). Thus, it is of great importance to improve the present mean state model biases, to understand how the Walker Circulation will change under global warming, and how this will affect ENSO amplitude. Acknowledgements We acknowledge the World Climate Research Program’s Working Group on Coupled Modeling, the individual mod- eling groups of the Coupled Model Intercomparison Project (CMIP5, CMIP6), the UKMetOffice, ECMWF and NOAA for providing the data sets. This work was supported by the SFB 754 “Climate-Biochemistry Interactions in the tropical Ocean”, the Deutsche Forschungs Gemein- schaft (DFG) project “Influence of Model Bias on ENSO Projections of the 21st Century” through grant 429334714, and the BMBF project InterDec (Grant 01LP1609B). Funding Open Access funding enabled and organized by Projekt DEAL. Open Access This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. 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Phytoplankton Trend Responce to Anthropogenic Influence. Nature. 2022-06-26. Jorma Jyrkkanen

and cmbustioon Future phytoplankton diversity in a changing climate. My Zoologist Friend in California says there has been a 40% decline in the oceans of phytpankton and starved baleen whales are washing up on shores.I have made a tentative link between this and combustion increase in decline of atmospheric oxygen Download PDF Article Open Access Published: 10 September 2021 Future phytoplankton diversity in a changing climate Stephanie A. Henson, B. B. Cael, Stephanie R. Allen & Stephanie Dutkiewicz Nature Communications volume 12, Article number: 5372 (2021) Cite this article 9038 Accesses 8 Citations 48 Altmetric Metrics details Abstract The future response of marine ecosystem diversity to continued anthropogenic forcing is poorly constrained. Phytoplankton are a diverse set of organisms that form the base of the marine ecosystem. Currently, ocean biogeochemistry and ecosystem models used for climate change projections typically include only 2−3 phytoplankton types and are, therefore, too simple to adequately assess the potential for changes in plankton community structure. Here, we analyse a complex ecosystem model with 35 phytoplankton types to evaluate the changes in phytoplankton community composition, turnover and size structure over the 21st century. We find that the rate of turnover in the phytoplankton community becomes faster during this century, that is, the community structure becomes increasingly unstable in response to climate change. Combined with alterations to phytoplankton diversity, our results imply a loss of ecological resilience with likely knock-on effects on the productivity and functioning of the marine environment. Introduction The socio-economic services provided by marine ecosystems are critical to human wellbeing. For example, fisheries provide almost half of Earth’s population with at least 20% of their animal protein intake1. Marine ecosystems also regulate Earth’s climate by absorbing and sequestering atmospheric CO2. Therefore, maintaining biodiversity is critical to providing resilience against future climate change and extremes2. At a global scale, biodiversity loss is being driven by human activities3,4, although clear trends of biodiversity decline in local ecosystems have proven difficult to identify5,6,7. Rather, the dominant species appear to be rapidly turned over, resulting in widespread reorganisation of ecosystems. These changes are potentially even more pronounced in the oceans than in the terrestrial realm8. In addition to human pressures on habitat, anthropogenic climate change is likely to drive biodiversity loss and hence decrease ecosystem stability2,9, thus affecting both the functioning and structure of marine ecosystems10,11,12. Ocean warming and alterations to nutrient supply via changing circulation or stratification, combined with additional stressors such as ocean acidification and deoxygenation, are likely to force community reorganisation. Predicting future changes to marine ecosystems is challenging, partly due to the relative paucity of consistent, repeated sampling, the inherent variability over daily to interannual scales in community composition13,14, and the lack of knowledge of how future climate change and other anthropogenic stressors may combine to alter biodiversity15. However, with future oceans predicted to be ~ 2−4 °C warmer, more acidic, and reduced in oxygen concentration16, species must adapt, migrate to regions of analogous conditions, or face extinction17,18,19. The expected resulting changes to biodiversity are likely to affect fundamental ecosystem functioning and processes, such as biomass production and maintaining water quality20,21,22, as well as the entire marine ecosystem structure, with consequences for the ocean’s capacity for food production and climate regulation23. As the base of the marine food web, phytoplankton play a fundamental role in setting the productivity of the entire marine ecosystem. Specific phytoplankton groups also play key roles in the biogeochemical functioning of the ocean; for example, by fixing atmospheric nitrogen (diazotrophs) or silica cycling (diatoms). Additionally, the size structure of the community affects trophic interactions, food web productivity, and carbon sequestration potential24,25,26. Here, we explore how phytoplankton diversity responds to a high emissions climate change scenario, similar to RCP8.527,28, using a marine ecosystem model with 35 phytoplankton types and 16 zooplankton size classes29,30,31, which are able to reorganise in response to changing oceanic conditions (see “Methods”). This model thus provides a more mechanistic representation of phytoplankton community structure than correlative or niche modelling approaches32,33,34, and greater realism than Earth System Models (ESMs) used for IPCC projections35,36.

Niche models and correlative approaches, by necessity, assume that the contemporary relationships between environmental conditions and phytoplankton abundance or diversity will remain the same in the future. These approaches do not have a mechanistic basis, and so changes in phytoplankton diversity driven by factors other than those included in the analysis (such as temperature, latitude, etc.), or conditions outside the bounds of variability in the contemporary ocean, cannot be reliably deduced. ESMs typically employ a very simplified ecosystem model, usually incorporating only 2 or 3 phytoplankton types. These models thus capture only a very limited diversity of phytoplankton communities. ESM results have focused on the response of phytoplankton to changing nutrient supply via changing stratification and circulation, which favours small species with high nutrient affinity37,38. However, in reality, phytoplankton respond to other factors which may result in changes to their relative competitiveness, or ultimately niche loss. Here, we use a complex ecosystem model with multiple functional groups of phytoplankton and several size classes of both phytoplankton and zooplankton types. Diversity in the model is set by several different mechanisms: the ratio of the supply rate of different limiting nutrients, the supply rate of limiting nutrients, grazing pressure, and transport/mixing39. Previous analysis of the modelled diversity has demonstrated that the combination of limiting nutrient supply and grazing controls the number of size classes that co-exist, and the ratio of supply rates of limiting resources contributes to setting the number of co-existing functional groups39. Transport and mixing tend to increase local diversity31. Although this model incorporates considerably more complexity than climate models, nevertheless it can only capture a fraction of the huge diversity of phytoplankton in the real ocean. Specifically, we capture diversity within biogeochemical functional groups (for example, diatoms, diazotrophs, etc.) and size classes (Extended Data Fig. 1). However, we do not capture the diversity that arises due to other traits, such as thermal norms, morphology, or colony formation39. Thus, in this study, the terms ‘richness’ and ‘diversity’ reflect functional richness and diversity, and should be understood in the context of these two important trait axes within the many different axes that set biodiversity in the real ocean. In this study, we quantify the response of marine phytoplankton diversity to climate change, focusing on future projections of community composition and turnover. We apply a high emissions climate scenario to a complex marine ecosystem model to explore the global and regional changes in phytoplankton community composition.

Important Gases in our Atmosphere. Trends. 2022-06-26. Jorma Jyrkkanen

Arctic Ocean Methane is being Released from Clathrates by Warming Arctic Ocean off Siberia. Jorma Archival Posts

Rising Arctic Ocean temperatures cause gas hydrate destabilization and ocean acidification A. Biastoch, 1 T. Treude, 1 L. H. Rüpke, 1 U. Riebesell, 1 C. Roth, 1 E. B. Burwicz, 1 W. Park,1 M. Latif, 1 C. W. Böning, 1 G. Madec, 2 and K. Wallmann 1 Received 21 February 2011; accepted 8 March 2011; published 16 April 2011. https://oceanrep.geomar.de/id/eprint/13116/1/2011_Biastoch_etal_GRL_2011GL047222.pdf [ 1 ] Vast amounts of methane hydrates are potentially stored in sediments along the continental margins, owing their stability to low temperature – high pressure conditions. Global warming could destabilize these hydrates and cause a release of methane (CH 4 ) into the water column and possibly the atmosphere. Since the Arctic has and will be warmed considerably, Arctic bottom water temperatures and their future evolution projected by a climate model were analyzed. The resulting warming is spatially inhomogeneous, with the strongest impact on shallow regions affected by Atlantic inflow. Within the next 100 years, the warming affects 25% of shallow and mid‐depth regions containing methane hydrates. Release of methane from melting hydrates in these areas could enhance ocean acidification and oxygen depletion in the water column. The impact of methane release on global warming, however, would not be significant within the considered time span. Citation: Biastoch, A., et al. (2011), Rising Arctic Ocean temperatures cause gas hydrate destabiliza- tion and ocean acidification, Geophys. Res. Lett., 38, L08602, doi:10.1029/2011GL047222.

1. Introduction [ 2] Formed under low temperature – high pressure con- ditions [Tishchenko et al., 2005] vast amounts of methane hydrates are considered to be locked up in sediments of continental margins [Buffett and Archer, 2004; Klauda and Sandler, 2005]. In the Arctic Ocean (AO), hydrates are deposited at shallow water depths close to shelf edges, stabilized by year‐round cold temperatures [Hester and Brewer, 2009]. Because the Arctic has warmed consider- ably during the recent decades and because climate models predict accelerated warming if global greenhouse gas emissions continue to rise [Intergovernmental Panel on Climate Change (IPCC), 2007], a destabilization of shallow Arctic hydrate deposits has been debated [Reagan and Moridis, 2007; Kerr, 2010]. Methane (CH 4 ), a gas with a global warming potential ∼25 times higher than CO2 [IPCC, 2007], could be released from the melting hydrates and enter the water column and atmosphere [Krey et al., 2009]. Recent field studies indicate an increase in methane fluxes from submarine Arctic permafrost and the seafloor [Westbrook et al., 2009; Shakhova et al., 2010]. Our multi‐disciplinary analysis provides a closer look into regional developments of submarine Arctic gas hydrate deposits under future global warming scenarios and reveals where and over which time scales gas hydrates could be destabilized and affect oceanic pH, oxygen, and atmospheric methane. 2. Temperature Evolution in the Mid‐depth Arctic Ocean [ 3 ] For an evaluation of the general distribution and the natural variability we investigated the spatio‐temporal vari- ability of Arctic bottom water in a hindcast experiment with the ocean/sea‐ice model NEMO (v2.3) [Madec, 2006], carried out by the DRAKKAR collaboration [The DRAKKAR Group, 2007]. The global simulation was performed at 1/2° reso- lution (ORCA05) and 46 levels in the vertical, whereby partial bottom cells allowed realistic topographic slopes. The experiment, that demonstrated its fidelity in simulating the salient features of the Atlantic circulation variability [Biastoch et al., 2008], was forced by inter‐annually varying atmospheric boundary conditions of the past decades [Large and Yeager, 2004]. To exclude a potential model drift in the water masses a second experiment under repeated‐year forcing was subtracted from the hindcast. The bottom water temperatures to first order reflect water depth (Figure 1a), featuring colder values around 0°C below 1000 m and warmer values on the shelves. However, a clear impact of the ocean circulation is seen as a band of temperatures around 1°C surrounding the AO at ∼400 m, an expression of the Atlantic inflow below the Arctic halocline [Polyakov et al., 2004]. Colder temperatures appear on the Russian and Canadian shelves due to the exposure of the surface waters to continental cold air outbreaks during winter. [ 4 ] The Atlantic inflow from the European Nordic Seas (ENS) into the AO exhibits pronounced variability on decadal time scales [Biastoch et al., 2008], following tem- perature and transport changes in the branch of the North Atlantic Current flowing through the ENS [Holliday et al., 2008]. The flow of Atlantic water towards the AO south of Svalbard (Figure 2a) shows a remarkable consistency with observations, both in mean temperature (3.70 ± 0.60°C vs. 3.96 ± 0.69°C [Holliday et al., 2008]) and variability, with minima in the late 1970s, mid 1980s and late 1990s. Changes towards warmer temperatures were reported for the past few decades [Holliday et al., 2008], which are sup- ported by the simulated long‐term trend (0.014°C yr−1 ). Although the long‐term trend (<0.005°C yr−1 ) of the bottom water is weaker (Figure 2b), a decadal variability by the Atlantic inflow is also present: changes over a single pentad repeatedly reach 0.75°C (red lines). The inflow signal extends to the shelf areas off Russia as part of the cyclonic circu- 1 Leibniz-Institut fu ̈r Meereswissenschaften an der Universita ̈ t Kiel (IFM-GEOMAR), Kiel, Germany. 2 Laboratoire d’Oce ́ anographie et du Climat: Expe ́ rimentation et Approches Nume ́ rique, Paris, France. Copyright 2011 by the American Geophysical Union. 0094‐8276/11/2011GL047222 GEOPHYSICAL RESEARCH LETTERS, VOL. 38, L08602, doi:10.1029/2011GL047222, 2011 L08602 1 of 5 lation around the AO [Dmitrenko et al., 2008]. Although the Arctic Intermediate Water also varies on a decadal time scale [Polyakov et al., 2004], bottom water temperatures along the Russian slope remain almost unaffected (Figure 2c). Only the shallow and potentially methane‐rich [Shakhova et al., 2010] shelf regions in the Laptev Sea show significant annual variations. [ 5] The future evolution of bottom water temperatures was analyzed in an ensemble of greenhouse warming integrations with a coupled climate model (KCM) [Park et al., 2009]. This configuration utilizes the same numerical framework, but at lower resolution (ORCA2, 2° horizontally, 31 levels) and the atmospheric model ECHAM5 [Roeckner et al., 2003] as an active atmosphere. In addition to a 430 year control experiment with present day greenhouse gas con- centrations (CO 2 = 348 ppm), an ensemble of eight 100‐year long global warming simulations, each starting from dif- ferent states of the control run, were performed with 1% increase in the CO 2 equivalent concentration [Park et al., 2009]. The linear trend of the ensemble average was com- bined with the ORCA05 distribution. The temperature changes (Figure 1b) show a highly inhomogeneous distri- bution, with increases of 1–2°C along the continental slopes and even higher values on the shelves due to the direct influence from the atmosphere. Individual ensemble mem- bers resembles strong inter‐annual to decadal variability in the Nordic Seas (Figure S1 in Text S1 of the auxiliary material) due to different states of the Atlantic Ocean circulation, but all feature a consistent long‐term trend of 2.5°C per cen- tury.1 Anomalies take some decades to protrude into the Laptev Sea, depending on the state of the Arctic circulation [Polyakov et al., 2004]; consistent trends are starting typi- cally after 50 years. 3. Impact on Methane Hydrate Stability and Ocean Acidification [ 6 ] Methane hydrate stability in marine sediments is mainly a function of temperature and pressure [Tishchenko et al., 2005]. A thermodynamic analysis (Figure 3b) of selected Arctic regions illustrates that in the ENS the methane hydrate will experience a phase shift from hydrate to free gas in mid‐depth levels at around 500 m within the next 100 years. Natural decadal variability can easily add another 0.75°C (Figure 2) to the long‐term increase. Along the Russian slope only shallower depths (∼300 m) undergo a phase shift. [ 7 ] For the overall impact of future bottom water warming on the stability of methane hydrates potentially stored in the Arctic seafloor we explored the thickness of the gas hydrate Figure 1. Map of the (a) time‐mean (1985–2004) bottom water temperatures in the ocean hindcast simulation and (b) ensem- ble‐mean trend in (in °C per 100 years) in the climate model simulation under CO 2 increase. The contour line depicts the 400 m isobath. The Laptev shelf area used for Figure 2c is marked by black stippling. Acronyms mark the Arctic Ocean (AO), European Nordic Seas (ENS) and the Laptev Sea (LS). 1 Auxiliary materials are available in the HTML. doi:10.1029/ 2011GL047222. BIASTOCH ET AL.: ARCTIC OCEAN GAS HYDRATES L08602L08602 2 of 5 stability zone (GHSZ) below the seafloor. The GHSZ is defined as that part of a sediment column where hydrostatic fluid pressures are higher than the temperature and salinity dependent dissociation pressure of gas hydrates. The dis- sociation pressure was calculated according to Tishchenko et al. [2005] using the fields from the ocean model and steady‐state temperatures computed from global heat flow values in combination with an average sediment conduc- tivity of 1.5 W m−1 K−1 for present (Figure S2) and future climates (Figure 3a). To roughly estimate the amount of hydrate within the GHSZ we used simple constant mean hydrate pore filling estimates of 2.4% (60–70°N) to 6.1% (north of 70°N) based on ODP data and numerical modeling [Klauda and Sandler, 2005]. Inhibition of hydrate formation by sulfate reduction is approximated by including a 5 m thick hydrate free zone below the seafloor. Assuming a mean porosity of 0.5 and standard values for density and methane content of hydrate, we estimated a total inventory of 900 Gt carbon (C) north of 60°N for the present climate. This value is not too far off the estimated 500 Gt C based on studies offshore Alaska [Kvenvolden, 1988] representing a fraction of the still largely unknown global hydrate inventory of 500–64,000 Gt C [Hester and Brewer, 2009]. Under the global warming scenario most affected regions are distributed around the AO and the ENS. Areas exhibiting decreases ≥20 m in the GHSZ thickness sum up to a total size of ∼850,000 km2 resulting in a total methane release of ∼100 Gt C. However, these estimates are too high for the considered 100‐year time window and need to be adjusted for the sluggish diffusion of heat into marine sediments. Using a constant thermal diffu- sivity of 4 × 107 m s−2 and neglecting the latent heat of hydrate melting, we find that only 12% of the worst‐case hydrate volume is reduced after 100 years for sulfate reduction zone thicknesses 5 m (Figure 3c). Note that sensitivity runs with 0 and 10 m sulfate reduction zone thicknesses show reductions of 14 and 10%, respectively. [ 8 ] What could happen to the released methane? It is conceivable from environmental hydrate studies that, depending on the release rate, at least ∼50% of the methane that dissolves into the sediment porewater, could be retained inside the seafloor by microbial anaerobic oxidation of methane (AOM) [Knittel and Boetius, 2009; Treude et al., 2003]. AOM represents a long‐term sink for methane‐ derived carbon, converting methane into bicarbonate and eventually precipitating it as authigenic carbonates [Peckmann et al., 2001]. However, methane rising through sediments as free gas could bypass the benthic methane filter [Knittel and Boetius, 2009] and, depending on water depth [McGinnis et al., 2006], immediately reach the atmosphere. Methane that on the other hand dissolves into the water column could be utilized by microbial aerobic oxidation of methane [Valentine et al., 2001]. Different to its counterpart AOM in sediments, aerobic oxidation of methane converts methane with oxygen into CO2 – a molecule that can impact oceanic pH. [ 9 ] For the following scenario we assume that 50% of the methane from the transient GHSZ thickness change is released into the water column and consumed by aerobic methanotrophs. A Lagrangian analysis of the oceanic cur- rents (auxiliary material) shows that (within a given year) the bulk of the water affected by methane is kept within 100 m above the bottom and along the mid‐depth topographic slope. Changes in seawater carbonate chemistry were cal- culated by adding the microbial produced CO2 to the back- ground dissolved inorganic carbon (auxiliary material). Some areas of the AO revealed pH values to drop by up to 0.25 units (Figure 4) within the next 100 years. Additionally, the aerobic consumption of methane could locally decrease bottom water oxygen concentrations by up to 25% (auxiliary material, data not shown). Regional methane‐induced sea- water acidification from the seafloor would occur in addition to an ocean‐wide acidification caused by the uptake of anthropogenic CO 2 from the atmosphere [IPCC, 2007]. The combined effect of the two processes would accelerate ocean acidification in parts of the AO, including deeper waters which otherwise would be exposed to ocean acidification with a considerable time delay. Research on that topic so far has been conducted under the premises of a projected pH decrease due to the anthropogenic CO 2‐uptake of about 0.3 units until the end of this century. Methane‐induced acidi- fication could nearly double this decrease in parts of the AO. [ 10] If, in a rather unrealistic scenario, all of the liberated methane would reach the atmosphere, global warming could be amplified [Bartdorff et al., 2008]. Under transient con- ditions we estimated an additional average methane flux of only 162 Mt CH4 yr−1 from melting Arctic hydrates over the next 100 years (auxiliary material) – a value lower than the current anthropogenic input of (600 Mt yr−1 ) [Bartdorff et al., 2008]. Sensitivity experiments with the climate model confirm the negligible feedback of the climate system under Figure 2. Variability of temperatures in the hindcast simu- lation, shown by monthly and inter‐annually filtered tem- peratures of (a) the Atlantic inflow (50–200 m depth) off Svalbard and bottom water temperatures (b) along the east- ern continental slope in the ENS off Svalbard and Norway (water depth 416–793 m) and (c) along the Russian conti- nental slope, (black, 416–793 m, 90–180°E) and on the shelf (blue, 0–100 m) in the Laptev Sea. The red lines mark trends in particular 5‐year periods. BIASTOCH ET AL.: ARCTIC OCEAN GAS HYDRATES L08602L08602 3 of 5 this limited additional amount of methane (Figure S3). On a longer time scale, however, the transient heat conduction leads to a faster methane release; the methane released from the steady‐state GHSZ calculation causes an upper limit of 0.8°C increase in surface air temperature on top of global warming. 4. Conclusions [ 11] The present study is to our knowledge the first combining ocean hindcasts and future climate projections with GHSZ calculations and potential consequences. It should be noted that the overall model still has its limitation with respect to the resolution of the bottom water tem- peratures, the actual distribution of sub‐seafloor methane hydrates and the individual response of the microbial com- munity in the sediment and water column. Nevertheless, the study clearly shows that hydrate destabilization can occur in the Arctic in response to global warming, and that the potential methane release is substantial, but limited in the next 100 years. An important finding is that warming and variability of the Atlantic inflow will play a major role in the fate of Arctic gas hydrates. Recent observations [Westbrook et al., 2009; Reagan and Moridis, 2009] agree well with sensitive areas identified here. Our maps could represent a useful tool in identifying areas around the Arctic Ocean where increases in methane release are likely to occur now or in the near future. [ 12 ] Acknowledgments. This research was part of the Custer of Excellence “The Future Ocean” funded by the German Research Founda- tion (DFG). The integrations of the experiments have been performed at the Computing Centre at Kiel University. [ 13] The Editor thanks one anonymous reviewers for their assistance in evaluating this paper. Figure 4. Changes in pH due to the release of 50% of the methane from hydrates within the first 100 years and distrib- uted over the first 100 m above the bottom. Figure 3. (a) Changes in thickness of the GHSZ caused by temperature increase of the ensemble mean of the global warm- ing, (b) phase diagram of methane hydrate as a function of pressure and temperature (constant salinity of S = 35 p.s.u.). Open symbols mark the bottom water temperatures along the ENS (cycles) and Russian (squares) slopes in the present cli- mate run, closed symbols the greenhouse warming experiments. Vertical bars indicate the vertical resolution of the ocean model. (c) Volumetric GHSZ thickness changes north of 60°N as a function of time. A value of 100% corresponds to the worst case scenario. The shaded range marks estimates for 0 and 10 m sulfate reduction zone thickness. BIASTOCH ET AL.: ARCTIC OCEAN GAS HYDRATES L08602L08602 4 of 5 References Bartdorff, O., K. Wallmann, M. Latif, and V. Semenov (2008), Phanero- zoic evolution of atmospheric methane, Global Biogeochem. Cycles, 22, GB1008, doi:10.1029/2007GB002985. Biastoch, A., C. W. Böning, J. Getzlaff, J.‐M. Molines, and G. Madec (2008), Causes of interannual‐decadal variability in the meridional over- turning circulation of the mid‐latitude North Atlantic Ocean, J. Clim., 21, 6599–6615, doi:10.1175/2008JCLI2404.1. Buffett, B., and D. 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Yeager (2004), Diurnal to decadal global forcing for ocean and sea‐ice models: The data sets and flux climatologies, NCAR Tech. Note NCAR/TN‐460+STR, Natl. Cent. for Atmos. Res., Boulder, Colo. Madec, G. (2006), Nemo ocean engine, note du pole de modelisation, Tech. Rep. 27, Inst. Pierre Simon Laplace, Paris. McGinnis, D. F., J. Greinert, Y. Artemov, S. E. Beaubien, and A. Wüest (2006), Fate of rising methane bubbles in stratified waters: How much methane reaches the atmosphere?, J. Geophys. Res., 111, C09007, doi:10.1029/2005JC003183. Park, W., N. Keenlyside, M. Latif, A. Ströh, R. Redler, E. Roeckner, and G. Madec (2009), Tropical Pacific climate and its response to global warming in the Kiel Climate Model, J. Clim., 22, 71–92, doi:10.1175/ 2008JCLI2261.1. Peckmann, J., A. Reimer, U. Luth, C. Luth, B. T. Hansen, C. Heinicke, J. Hoefs, and J. Reitner (2001), Methane‐derived carbonates and authigenic pyrite from the northwestern Black Sea, Mar. Geol., 177, 129–150, doi:10.1016/S0025-3227(01)00128-1. Polyakov, I., G. Alekseev, L. Timokhov, U. Bhatt, R. Colony, H. Simmons, D. Walsh, J. Walsh, and V. Zakharov (2004), Variability of the interme- diate Atlantic water of the Arctic Ocean over the last 100 years, J. Clim., 17, 4485–4497, doi:10.1175/JCLI-3224.1. Reagan, M. T., and G. J. Moridis (2007), Oceanic gas hydrate instability and dissociation under climate change scenarios, Geophys. Res. Lett., 34, L22709, doi:10.1029/2007GL031671. Reagan, M. T., and G. J. Moridis (2009), Large‐scale simulation of meth- ane hydrate dissociation along the West Spitsbergen Margin, Geophys. Res. Lett., 36, L23612, doi:10.1029/2009GL041332. Roeckner, E., et al. (2003), The atmospheric general circulation model ECHAM5: Part 1. Model description, Rep. 349, Max Planck Inst. for Meteorol., Hamburg, Germany. Shakhova, N., I. Semiletov, A. Salyuk, V. Yusupov, D. Kosmach, and O. Gustafsson (2010), Extensive methane venting to the atmosphere from sediments of the East Siberian Arctic Shelf, Science, 327, 1246–1250, doi:10.1126/science.1182221. The DRAKKAR Group (2007), Eddy‐permitting ocean circulation hind- casts of past decades, CLIVAR Exchanges, 12, 8–10. Tishchenko, P., C. Hensen, K. Wallmann, and C. S. Wong (2005), Calcu- lation of the stability and solubility of methane hydrate in seawater, Chem. Geol., 219, 37–52, doi:10.1016/j.chemgeo.2005.02.008. Treude, T., A. Boetius, K. Knittel, K. Wallmann, and B. Jørgensen (2003), Anaerobic oxidation of methane above gas hydrates at Hydrate Ridge, NE Pacific Ocean, Mar. Ecol. Prog. Ser., 264, 1–14, doi:10.3354/ meps264001. Valentine, D. L., D. C. Blanton, W. S. Reeburgh, and M. Kastner (2001), Water column methane oxidation adjacent to an area of active hydrate dis- sociation, Eel River Basin, Geochim. Cosmochim. Acta, 65, 2633–2640, doi:10.1016/S0016-7037(01)00625-1. Westbrook, G. K., et al. (2009), Escape of methane gas from the seabed along the West Spitsbergen continental margin, Geophys. Res. Lett., 36, L15608, doi:10.1029/2009GL039191. A. Biastoch, C. W. Böning, E. B. Burwicz, M. Latif, W. Park, U. Riebesell, C. Roth, L. H. Rüpke, T. Treude, and K. Wallmann, Leibniz‐Institut für Meereswissenschaften an der Universität Kiel (IFM‐GEOMAR), Düsternbrooker Weg 20, D‐24105 Kiel, Germany. (abiastoch@ifm‐ geomar.de) G. Madec, Laboratoire d’Océanographie et du Climat: Expérimentation et Approches Numérique, 4, place Jussieu, F‐7525 Paris CEDEX 05, France. BIASTOCH ET AL.: ARCTIC OCEAN GAS HYDRATES L08602L08602 5 of 5

Climate Change Adaptation in the Okanagan Conference. Kelowna BC. May 31, 2002. Jorma Jyrkkanen's Notes.

Beyond Emissions. Kelowna Climate Change Forum Synopsis. Beyond Emissions: Climate Change Forum; What Can We Expect From Climate Change? May 30th and May 31st 2002; Kelowna BC

The following are my notes from the Forum, and submissions by Presenters so by all means go to the source for definitive works. Thursday, May 30 at OUC KLO Campus Theater 7:00 PM Introduction 7:15: Wendy Avis, Environment Canada-Canada’s climate change status and programs. The positions are well covered and critiqued by Author Guy Dauncy below. The Canadian government is looking for inputs from Canadians this summer in an effort to firm up its position. Downloadable copies of Canada Position with Emission Data and Trends; Also Resources for Action and Draft Plan for Kyoto (New on Oct. 24th, 2002): http://www.climatechange.gc.ca http://www.ec.gc.ca/pdb/ghg/ghg_docs/gh_eng.pdf 7:30 Jenny Fraser, jenny.fraser@gems8.gov.bc.ca Climate Change Section, Ministry of Water, Land and Air Protection topic: Impacts of climate change during the 21st Century-An overview of changes in temperature, precipitation, and impacts on related systems across BC. The study concurs that climate change is happening and graphs of CO2 and temperature going back 1000 years confirm the trends and show that the major changes have happened since the industrial revolution. Provincial trends demonstrate statistically significant trends in most climate change parameters that were considered with generally higher levels in parameters further north. The 'noise' created by El Nino, La Nina, and the Pacific decadal oscillation (PDO) were filtered by examination of time trends to extract climate change influences. Past impacts from 100 years of historical data and projections for the next century are that: 1. Average annual temperature warmed by 0.6 deg C on the coast, 1.1 deg C in the interior, 1.7 deg C in northern BC. Averge overall to increase by 1 to 4 deg C. 2. Night-time temperatures increased across most of BC in spring and summer. [Will probably increase-JJ] 3. Precipitation increased by 2 to 4 % per decade. It may increase by 10 to 20%. 4. Lakes and rivers became free of ice earlier in the spring. Some interior rivers may dry up during summer and early fall. 5. Sea level temperatures increased by 0.9 deg C to 1.8 deg C along the BC coast. [Will probably increase-JJ] 6. Sea level rose by 4 to 12 cm along most of the BC coast. [Will probably increase-JJ] 7. The Fraser river discharges more of its annual flow earlier in the years. [Will probably get even earlier-JJ] 8. Water in the Fraser river is warmer in summer. Salmon migration patterns are likely to change. 9. More heat energy is available for plant and insect growth. The mountain pine beetle is likely to expand its range. 10. Two large BC glaciers retreated by more than a kilometre each. Small ones are expected to disappear in the next century. Climate change may influence the frequency of extreme weather events, extent of permafrost, ecosystem structures and processes, species distribution and survival. All of this will have effects on fish, wildlife, plant and human society. [For example, though they don't mention it, in BC coastal waters, we are finding tropical predatory fish for the first time, and Cattle Egrets from Africa in Squamish, and on Vancouver Island, the deadly tropical disease, Cryptococcus neoformans while in the Niagra area of Ontario, West Nile Virus. These findings suggest climate change influences on range expansion of beneficial species as well as pathogens. For info on El Nino and PDO-JJ] http://eos-chem.gsfc.nasa.gov/instruments/mls/elnino.html http://tao.atmos.washington.edu/pdo/graphics.html http://www.cpc.ncep.noaa.gov/products/analysis_monitoring/enso_update/gsstanim.html Downloadable copies of detailed study located at: http://www.gov.bc.ca/wlap http://wlapwww.gov.bc.ca/air http://wlapwww.gov.bc.ca/air/climate/index.html#indicators 7:45 Tina Neale, tneale@sdri.ubc.ca Sustainable Development Research Institute UBC Potential impacts of climate change on water resources in the Okanagan based on 2001 research paper 'Water Management and Climate Change in the Okanagan Basin' by Stewart Cohen and Tanuja Kulkarni. Environment Cda and UBC. Development of climate change impact scenarios on hydrology in 2002 and 2004. Temperature to rise 1-2.5 deg C from 1961-1990 base period to the 2020's, and 3-5 deg C by 2080's. Higher precipitation is expected for winter, but models differ on what will happen in summer. Recent warming has led to changes in stream flow. Observed changes in unregulated streams include earlier onset of the annual spring peak by as much as four to six weeks, lower peak volumes, lower fall flows and higher early winter flows with all areas showing loss of winter snowpack. Regulated streams are showing decreases in discharge. Earlier peak flows and lower flows in lower elevations streams with low late summer flows. In higher elevation streams there may be more water or the same but flow timing is affected. No consensus on scenario changes to total annual flows. Structural measures were options chosen by stake holders when considering adaptation mechanisms and comments indicated a need for more research and outreach. Copies of study available at (2.676K pdf format): http://www.sdri.ubc.ca http://www.sdri.ubc.ca/documents/Water_Management_and_Climate_Change_in_the_Okanagan_Basin.pdf 8:15 Dr. Milt McLaren, SFU Professor Emeritus, Climate Change Education-Why is it difficult to teach about climate change? We need timely public response to this issue. Why is this so challenging? Misconceptions abound and we don't need more science to prove anything. We just need to present existing findings in a way that people can understand so that misconceptions are cleared up. Most troubling were his comments on methane reservoirs tied up in the ocean's continental shelves [clathrates or methane hydrates-JJ] which can be released by a rise of only 0.5 deg C in sea temperature and the methane tied up by tundra reservoirs in permafrost which can also be released by global warming. When the methane, a [27X, (10-20X->by others)] more powerful Greenhouse gas than CO2, is released by global warming and the volumes are plugged in by Modelers, he said there was a tipping point which launched a positive feed-back loop leading to the run-away dooms-day scenario and we might end up looking like Venus. Too hot for life. References on this critical issue: http://www.ipcc.ch/pub/tar/wg1/134.htm http://www.nsc.org/ehc/climate/ccucla5.htm http://www.gaiabooks.co.uk/environment/tundra_warming.html http://www.asf.alaska.edu/daac_documents/cdrom_docs/30228.html http://www.iclei.org/efacts/greengas.htm http://www.ucmp.berkeley.edu/education/events/cowen1c.html http://ethomas.web.wesleyan.edu/ees123/clathrate.htm http://www.mbari.org/ghgases/peerart/hydmodel.html Apparently, tundra wetlands are a massive source of methane and tundra forests are a sink. Which will predominate in global warming is then the question as is the question of what will ocean warming do to the liberation of submarine methane reservoirs and how soon will it happen at present trends? A critical problem assessment needs to be done because there is conflicting data out there.-JJ Clearly we need to do whatever is needed at whatever cost or sacrifice, to ensure that ocean methane reservoirs don't burp. Q? Are there other surprises lurking with the other identified greenhouse gases; ie: nitrous oxide, hydrofluorocarbons, perfluorocarbons, and sulphur hexafluoride, CO2, viz. a viz. release reservoirs? Makes one realize how dangerous President Bush's and Prime Minister Cretien's waffling on environment is.-JJ] 8:30 Dr. Mindy Brugmann, glaciologist-Climate Change Research on Illecilleweat Glacier, Glacier National Park. Mindy said basically that Illecilleweat glacier is retreating as are most glaciers in BC and it is also thinning. These changes are attributed to climate change and are greatly influenced by short term and long term oceanic temperature cycles which in turn are influenced by global warming. There will be important and negative impacts on a variety of parameters including late summer water flow, peak flows, timing of flows, volume of flow, local climate- to name a few. I mentioned the Blake expeditions to the Himalayas and the fact that they found that the melt is highest at higher elevations and Himalayan lakes are threatening to overflow and wipe out huge numbers of people down slope. [My question is: Will global warming temperatures rise more sharply when the majority of glaciers have vanished as they seem to be doing and may the loss of their their moderating influence make a tipping event scenario more likely? Comment: small glaciers have vanished in the past century. See http://www.geocities.com/jormabio/archive/disappearing_glaciers.html -JJ] Coverage of previous similar presentations: http://www.tv.cbc.ca/national/pgminfo/glacier http://www.cmiae.org/research/climatechange.htm Friday, May 31st at Okanagan Regional Library 1380 Ellis Street 9:30 – 4:30 p.m.. Speakers, Workshop and Panel 9:30 Dr. Andrew J. Weaver, FRSC weaver@uvic.ca U Victoria, School of Earth and Ocean Scientists-Topic: What is climate change? Climate change is the statistical analysis of trends in climate parameters, not our subjective assessment of weather based upon memory. Long term measurements are the only way to get at the information and media make mockery of the science by posting nonsensical anecdotal arguments of non-believers and believers. Science is the solution to arguments. Yes, global warming is happening and CO2 has always been involved in it. IPPC 2001 says that there is "New and stronger evidence in the past 50 years that temperature increases are attributable to humans." Global mean 370 ppm is 70 ppm over max ever seen before during interglacial. Both methane and CO2 have shown major peaks in the past 400,000 years and these coincided with major warming during the interglacial epochs. Climate warms during the interglacial due to rising CO2 and cools during the glacial periods but, we have never seen anything like the CO2 levels at present, so there is a real issue to deal with. The Northern hemisphere is, relative to southern latitudes, more affected by greenhouse gases as is the land area and night-time lows are increasing faster than daytime highs. Storm events are increasing in frequency and this will lead to huge costs in infrastructure and insurance. Future projection is 2 degrees Celsius by 2100 if no Kyoto. If Kyoto goes ahead as per present target, temperature will be 1.92 deg C. Kyoto must be strengthened and the goal needs to be strengthened to 4X pre industrial Greenhouse gases so we need to cut emissions by 50% by 2010. Basically, as is Kyoto is irrelevant. Antarctica is warming in the periphery but cooling in the center. No single event is attributable to GG. Policy works, CFC's are down and will be gone in 50 years from now. Make effective policy is the message. We need models and upgrades constantly. Mitigation is likely to fail and is pie in the sky since one solution will create another problem. He used the "The King and the Mice and the Cheese Game" analogy. The only solution was to quit eating cheese. Ergo we need to quit consuming hydrocarbons. [Climate engineering is being thought of by some as a solution to global warming. A reference for those interested in reviewing this controversial ecosystem damaging approach-JJ]: http://www.chooseclimate.org/cleng/cleng.html Alternatives that can work are nuclear, hydrogen fuel cell, and solar and wind. Tidal is being explored by Australia. "Combating global climate change is about global security!" (Investors start moving your money-the best brains are giving a clue to market futures here! However, nuclear is bound to be controversial because of the potential pollution by radio-active substances. Jorma Comment). Dr. Weaver's Climate Website: http://wikyonos.seos.uvic.ca/climate-lab.html 10:00 Guy Dauncy, Author, guydauncy@earthfuture.com http://www.earthfuture.com Author Stormy Weather-topic: Climate Change Solutions. Guy favors a positive approach and also using regulations to drive switching to alternate energy technological innovation and he says there is evidence that investment will follow. Targeted measures are best and do it bit by bit. First in to the alternate technology will capture leadership in global market share and can sell the technology to the world. Heritage solar shingles will be competitive in cost and efficiency possibly as soon as 2005 at $1.00/Watt. 10,000 m^2 of Nevada at 17% efficiency, can supply all of the US power requirements. http://www.zeenrgy.com. North sea aricity of offshore wind shows it as viable alternative and BC has similar potential for wind. Vehicles can go hydrogen gas electric (HGE) for 80 mpg and save enormously on GG's. Alternatives work and can save us. Investment should start immediately. Sinks are a scam and credits are a cop-out, and oil companies have shown that they can increase their efficiency enormously and thereby save on GG's and GW. For full and actual text see below. Full detailed talk available at: http://www.geocities.com/jormabio/climate/guy_skelownapaper.doc 10:30 Jim Vanderwal, jvanderwal@fraserbasin.bc.ca BC Climate Exchange (formerly the HUB Public Education and Outreach Office) and Fraser Basin Council. Jim gave a number of web sites where good things can be found. His organization acts through improving education and corporate behavior. they are involved in transportation, education and teachers. He talks about sustainable industry, energy efficiency, green buildings, workplace education, renewable energy, smartgrowth, and mobilization by connecting to personal interests, leadership and by providing a clearinghouse for educational resources. He provides web sites which promote aspects of their approach and his group is actively involved in web site development. Web Sites of interest suggested by Jim: http://www.betterbuildings.ca http://www.bcyhdro.com http://www.kepp.org http://www.energy.ca http://www.energyaware.bc.ca http://www.best.bc.ca http://www.nccp.ca http://www.fraserbasin.bc.ca 10:45 Rob Scherer, Forest Research Extension Partnership (FORREX), Extension Specialist with the Watershed Management Program at Okanagan University College. Rob talked about Dr. Peter Dill's work with Kokanee which are being stressed by increasing stream temperatures. Eggs are killed by stream temps over 14 deg C and these are more and more common in recent times. Spawning success also affected. Looking for solutions like using reservoirs to maintain water supply. A guest raised the question of impacts of climate change on forestry. Two issues arose. Bark beetles and migration of ecosystems. I had researched this issue previously and mentioned that bark beetles are in epidemic now because extremes of cold temperature are no longer happening due to global warming and so the beetles are surviving winters. Adding to this are fire protection forest management practices which have created huge stands of vulnerable mature pine, and therefore the cost of beetle recovery is directly attributable to global warming. [Fires used to take these stands out naturally in pre-contact times.] I mentioned also that hardwood forests are migrating northwards displacing our softwoods. I disagreed with him on the ability of ecosystems to adapt in all cases. I had seen modeling work from American scientists predicting that ecosystem migration rates may exceed the adaptation rate of many species. The Chairman brought up the issue that tree species being planted today need to be those than can tolerate the changed ecosystems of tomorrow. [For forest management purposes in BC, ecosystems are differentiated on the basis of temperature, moisture and nutrients to name a few parameters, and temperature and moisture are certainly changing due to global warming phenomena]. Access information: http://www.forrex.org/home/home.asp http://royal.okanagan.bc.ca/kokanee/links.htm 11:00 Russ Haycock, http://www.fcm.ca Federation of Canadian Municipalities The FCM Partners for Protection program. Russ has been involved in 25 Municipal Greenhouse Action Plans and is a great contact for cities like Kelowna. Russ explained that there exists a Federation of Canadian Municipalities for Sustainable Development and they have Formed Partners for Climate Protection with objectives-Reduce Greenhouse gas emissions, Develop local action plans, lead by example and collaboration, and provide 50% Green funds for action plans. Municipalities can show leadership and make enormous saving in energy and reduce emissions by things like; building retrofits, diverting solid wastes, upgrading water and waste water treatment facilities, improving the vehicle fleets etc. He suggests that people get a Political Champion to help move these objectives through communities and that we think Global. Material available at: http://www.fcm.ca/newfcm/Java/frame.htm 11:15 Lunch 12:30 Marnie Olson BEd. and Deb Calderone, marnie.olson@gvrd.bc.ca Whats the Fuss? GVRD Climate Change Workshop. Material and support for education on climate change for teachers available at the web site below or by contacting Marnie or Deb. Excellent teaching resources and workshop techniques for teachers of Socials Studies or Earth Sciences 11 especially but also good for teacher training workshops on this subject. Brainstorming technique application. Really gets climate issues through to students. Four terrific booklets, Poster, and other resources available. The booklets are: 1. Lets Clear the Air, Intermediate air quality education program. GVRD. 2. What's all the fuss? Climate Change Teaching strategies with the 'Temperature Rising' poster for Southwestern British Columbia. A curriculum to explore the concepts of our connection to climate change concerns, causes, potential impacts and possible actions. 3. Pamphlet. Lets Clear the Air. A primary activity book. GCRD. 4. Pamphlet. Climate Change in the Classroom. 8 pages. Make sure you ask for the posters. Our Workshop study group reviewed sea level rise and came up with impacts including huge numbers of deaths, and dislocatons, increase in hunger and starvation and population densities inland, loss of fisheries and agriculture, increased costs of everything. Famine and disease. In southern BC we would lose Lulu Island, Delta, Richmond, Steveston; Roberts Bank, the International Airport at Vancouver, Ports and Estuary facilities all over BC, fisheries production from estuaries, and tsunami dangers would also increase enormously. All the homes and work places there would be rendered worthless. People would have to move and they would lose their jobs. Places like Indonesia and Bangladesh would lose enormous amounts of land and homes for people and liveli-hoods and many would die or be dislocated to refugee camps. Fish and wildlife and biodiversity would suffer enormously globally. Contact and materials at: http://www.gvrd.bc.ca 2:30 Water Issues Panel- Participants: Neil Klassen, Kelowna Water Smart; Up costs & Conservation message. stewardship@sylix.org; Michelle Boshard, Brian Symonds, Dr. Denise Neilson Phd, Research Scientist, Ag. Cda.NEILSEND@EM.AGR.CA Denise talked about forthcoming water issues for orchard tree crops related to projected climate warming and concluded that there would be a longer summer season with considerably hotter earlier spring and an extended fall. There would be an increase of 28% by 2050 for plant water demand and 37% increased irrigation demand. Demand will exceed supply in tributary drainage orchards. She discussed how we might adapt and cited a number of options a few of which include change species, move upslope, improve conservation, and promote subsurface drip. Contact and study available through:http://res.agr.ca/summer/parc.htm Howie Right; Okanagan Aboriginal Peoples' Fisheries Commission. Expressed specific concerns for Kokanee Fisheries Resource and issues surrounding development impacts and water supply. Also cut-backs by FRBC WRP for fisheries habitat restoration projects. Howie cited a number of WRP past projects completed. Severe decline of stocks has resulted from changes brought about by growth of human population in the Okanagan to the point where they are no longer used as a food fish. A participant informed me that there were pesticide toxicology studies underway on the Okanagan lake fish presently. Though Howie gave me no reference, for those interested in the plight of the kokanee I suggest the following link: http://royal.okanagan.bc.ca/kokanee/links.htm Wendy Avis, See above, Day 1. Tina Neale; tneale@sdri.ubc.ca Tina gave a mini presentation similar to yesterdays regarding research paper; 'Water Management and Climate Change in the Okanagan Basin' by Stewart Cohen and Tanuja Kulkarni. Environment Cda and UBC. Paper available from: scohen@sdri.ubc.ca Web Site Hosted by Jorma Jyrkkanen, Forum Participantjormabio@hotmail.com To see what is happening to global temperatures by hemisphere and Northern Hemisphere (NH) from dendroclimatology in particular; you may wish to visit the following sites: http://www.cru.uea.ac.uk/cru/climon/data/themi/ http://www.ngdc.noaa.gov/paleo/ei/ei_reconsa.html To monitor the climate see http://www.cru.uea.ac.uk/cru/climon/ The sun is in a hot cycle or trend at present so it appears to be having an impact on warming. Volcanic dust and gases have been postulated to cool equatorial waters in the past reducing the north-south sea temperature gradient thereby stopping ocean transport of heat and oxygen leading to a sinking of warm water and a disasterous methane degassing. To see what role volcanic dust and solar inputs might play relative to carbon dioxide and NH temperature see this site: http://www.ngdc.noaa.gov/paleo/ei/ei_reconsc.html To see what folks in California just south of us have found due to climate change and what they are thinking about and doing about global warming, check out this site: http://www.caglobalwarming.org/ What was noteworthy about this Forum is that none of the experts disputed that there was a problem or that we needed to take effective measures and to act more decisively than we have done to date. The 'methane hydrates' or 'clathrates' issue is not going to go away. It is an unknown in the global warming equation, with a devastating potential if we tweak the wrong buttons. The earth might be able to recover from a methane hydrate meltdown but probably not before Crockodilians were living in Norway again. If the cold water current stops circulating cold Arctic water to the Atlantic deep, there could be a severe warming of the tropical and temperate seas with devastating consequences for methane release from these clathrates. This scenario is one possibility after the Arctic Ice vanishes in about 2050 (My theory, 2004). More than anything, this issue points to the fact that we need a pro-active high profile green world leader to champion the cause of global warming and we need immediate commitments by all nations, rich and poor, to fight for a clean planet living in sustainable harmony with its ecosphere. Striking was the positive tone of this forum based on the fact that there are many solutions but we need to act and get the economy working alongside of science to solve these problems and create a survivable future for our planet. The opportunity created by these vital challenges is enormous and should be seen as a positive incentive for political policy and social infrastructure and money markets to move into efficiency technology and enhanced sustainability. This initiative would also enhance global security. Young people need to be at the leading edge of this movement. Another very useful organization to link with which enables researchers with funding and in developing adaptation action strategies is the C-CIARN BC Climate Exchange which can be accessed at BC-CIARN Jorma. original at http://www.geocities.com/jormabio/index.html now no longer operative. Copyright 2002 Jorma Jyrkkanen. All rights reserved. 0 00 Posts with tag Mercury Link to Arctic Fox Declines Probably due to Atmospheric Acidic Accumulation Sudden Formation of Greenland Ice Sheet Suggested by Pine in Basal Core. Global Atmospheric CO2 passes 400 ppm First Time in Two Million Years No comments yet Post a new comment jorma_jyrkkanen May 15 2013, 09:22 0 00 Mercury Link to Arctic Fox Declines Probably due to Atmospheric Acidic Accumulation Mercury Link to Arctic Fox Declines Probably due to Atmospheric Acidic Accumulation 15 May 2013 Arctic fox are in decline and the culprit is thought to be mercury. So why is it suddenly a problem? Its not. The problem has been building for some time. http://www.bbc.co.uk/news/science-environment-22425219 The Arctic is a collecting area for northern hemispheric global winds where gases in those winds cool and volatile substances condense and drop onto the substrate, be it water, ice or land. One of the biggest most dangerous components of those winds are the acids with pesticides a close runner up. There is sulfur dioxide (SO2), sulfuric acid (H2SO4) and many kinds of sulfur gases in abundance from various north hemispheric mills and there is also nitrous oxide (N2O) and the ever ubiquitous CO2. These are gaseous green house gas spin offs of fossil fuel combustion that excacerbate the impacts of global warming on animal species. When you acidify soils, bound mercury becomes labile and enters the water runoff. It is likely that the sequestering capacity of the oceans are already full for CO2 with conversion of CO2 into acid and return to the atmospheric buildup. The addition of these other anthropogenic acids probably contributes to reducing ocean sequestering even more and adds to higher acidity overall and increases mercury lability and mobility into the Arctic ocean and up the food chain. This is likely the story in the Arctic. When it reaches the oceans, bacteria convert it to more bioconcentrating forms which then move up the food chain and into those animals like seals and whales which Arctic fox feed upon. This problem is more acute in the arctic because there is circumpolar drainage into arctic waters from northern hemisphere rivers draining the zones of acid accumulation. Canada, the USA, China, Russia and the EU have many mills spewing these contaminants into global air sheds. Mercury once bound in the benthic sediment is possibly being made more labile and water soluble by bacterial action. Many predator fish have elevated mercury simply due to natural mercury in the environment. Excretion normally balances that out at survivable levels, but the balance is narrow for larger predators and can easily be exceeded. Tuna and Shark and Swordfish for example have naturally high levels. The bad news is that if fox are being polluted and reproduction is declining, then sea birds, fish eating eagles, polar bears and Orcas are probably not far behind because mercy affects not only nervous systems, but targets the reproductive systems of many species. Copyright Jorma Jyrkkanen. All rights reserved.