Forecasting climate-resilient conservation futures for the White-bellied Heron ( Ardea insignis ) in Bhutan using coupled model intercomparison project phase 6 climate scenario

The double‐intertropical convergence zone (ITCZ) bias is one of the most outstanding errors in all previous generations of climate models. Here, the annual double‐ITCZ bias and the associated precipitation bias in the latest climate models for Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) are examined in comparison to their previous generations (CMIP Phase 3 [CMIP3] and CMIP Phase 5 [CMIP5]). All three generations of CMIP models share similar systematic annual multi‐model ensemble mean precipitation errors in the tropics. The notorious double‐ITCZ bias and its big inter‐model spread persist in CMIP3, CMIP5, and CMIP6 models. Based on several tropical precipitation bias indices, the double‐ITCZ bias is slightly reduced from CMIP3 or CMIP5 to CMIP6. In addition, the annual equatorial Pacific cold tongue persists in all three generations of CMIP models, but its inter‐model spread is reduced from CMIP3 to CMIP5 and from CMIP5 to CMIP6.

Simulation of the carbon cycle in climate models is important due to its impact on climate change, but many weaknesses in its reproduction were found in previous models. Improvements in the representation of the land carbon cycle in Earth system models (ESMs) participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) include the interactive treatment of both the carbon and nitrogen cycles, improved photosynthesis, and soil hydrology. To assess the impact of these model developments on aspects of the global carbon cycle, the Earth System Model Evaluation Tool (ESMValTool) is expanded to compare CO2-concentration- and CO2-emission-driven historical simulations from CMIP5 and CMIP6 to observational data sets. A particular focus is on the differences in models with and without an interactive terrestrial nitrogen cycle. Overestimations of photosynthesis (gross primary productivity (GPP)) in CMIP5 were largely resolved in CMIP6 for participating models with an interactive nitrogen cycle but remain for models without one. This points to the importance of including nutrient limitation in models. Simulating the leaf area index (LAI) remains challenging, with a large model spread in both CMIP5 and CMIP6. The global mean land carbon uptake (net biome productivity (NBP)) is well reproduced in the CMIP5 and CMIP6 multi-model means. This is the result of an underestimation of NBP in the Northern Hemisphere, compensated by an overestimation in the Southern Hemisphere and the tropics. Models from modeling groups participating in both CMIP phases generally perform similarly or better in their CMIP6 version compared to their CMIP5 version. Emission-driven simulations perform just as well as the concentration-driven models, despite the added process realism. Due to this, we recommend that ESMs in future Coupled Model Intercomparison Project (CMIP) phases perform emission-driven simulations as the standard so that climate–carbon cycle feedbacks are fully active. The inclusion of the nitrogen limitation led to a large improvement in photosynthesis compared to models not including this process, suggesting the need to view the nitrogen cycle as a necessary part of all future carbon cycle models. Overall, a slight improvement in the simulation of land carbon cycle parameters is found in CMIP6 compared to CMIP5, but with many biases remaining, further improvements of models in particular for LAI and NBP is required. Due to the inclusion of the study in ESMValTool, the analysis can easily be repeated on the upcoming CMIP7 models to evaluate the progress from CMIP6.

An assessment is made of the modes of interannual variability in the seasonal mean summer and winter Southern Hemisphere (SH) 500-hPa geopotential height in the twentieth century in models from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) dataset. Modes of variability of both the slow (signal) and intraseasonal (noise) components in the CMIP5 models are evaluated against those estimated from reanalysis data. There is general improvement in the leading modes of the slow (signal) component in CMIP5 models compared with the CMIP phase 3 (CMIP3) dataset. The largest improvement is in the spatial structures of the modes related to El Niño–Southern Oscillation variability in SH summer. An overall score metric is significantly higher for CMIP5 over CMIP3 in both seasons. The leading modes in the intraseasonal noise component are generally well reproduced in CMIP5 models, and there are few differences from CMIP3. A new total overall score metric is used to rank the CMIP5 models over both seasons. Weighting the seasons by the relative spread of overall scores is shown to be suitable for generating multimodel ensembles for further analysis of interannual variability. In multimodel ensembles, it is found that an ensemble of size 5 or 6 is sufficient in SH summer to reproduce well the dominant modes. In contrast, about 13 models are typically are required in SH winter. It is shown that it is necessary that the selected models individually reproduce well the leading modes of the slow component.

California is one of the major uncertainty hotspots for climate change, as climate models have historically been split between projecting wetter and drier future conditions over the region. We analysed the future (mid‐century and end‐century) projections of California's winter precipitation changes from the latest Coupled Model Intercomparison Project Phase 6 (CMIP6), and studied its respective model agreement in comparison to the previous CMIP5 projections. Over northern California more than two thirds of the models in each ensemble agree on wetter future conditions. However, over southern California both ensembles show highly uncertain precipitation changes, with model projections almost equally divided between wetter or drier conditions. Projected end‐century precipitation changes range from −30% to +70% in CMIP5 and −20% to +80% in CMIP6. The CMIP6 ensemble mean changes are generally wetter and show larger model disagreement compared to CMIP5. Distribution of year‐to‐year precipitation indicates more extremely wet or dry years over southern California in CMIP6 compared to CMIP5, with some models suggesting that the five wettest years account for as much as ~55% of the 20‐year rainfall, and the five driest for as little as ~5%. Dynamically, both ensembles project weakened subsidence over Baja California that is stronger in CMIP6 than in CMIP5, in line with the wetter mean conditions in CMIP6. In the western tropical Pacific we find strengthening of the Hadley circulation in CMIP6 that is not seen in CMIP5, and more El Niño than La Niña conditions in the equatorial Pacific. More CMIP6 models also project an increase in ENSO events compared to CMIP5, and a stronger impact of ENSO on California's precipitation is found in CMIP6 than in CMIP5. These factors also contribute to larger model disagreement and more extremely wet or dry years over southern California in CMIP6.

Abstract. By regulating the global transport of heat, freshwater, and carbon, the Atlantic meridional overturning circulation (AMOC) serves as an important component of the climate system. During the late 20th and early 21st centuries, indirect observations and models suggest a weakening of the AMOC. Direct AMOC observations also suggest a weakening during the early 21st century but with substantial interannual variability. Long-term weakening of the AMOC has been associated with increasing greenhouse gases (GHGs), but some modeling studies suggest the build up of anthropogenic aerosols (AAs) may have offset part of the GHG-induced weakening. Here, we quantify 1900–2020 AMOC variations and assess the driving mechanisms in state-of-the-art climate models from the Coupled Model Intercomparison Project phase 6 (CMIP6). The CMIP6 forcing (GHGs, anthropogenic and volcanic aerosols, solar variability, and land use and land change) multi-model mean shows negligible AMOC changes up to ∼ 1950, followed by robust AMOC strengthening during the second half of the 20th century (∼ 1950–1990) and weakening afterwards (1990–2020). These multi-decadal AMOC variations are related to changes in North Atlantic atmospheric circulation, including an altered sea level pressure gradient, storm track activity, surface winds, and heat fluxes, which drive changes in the subpolar North Atlantic surface density flux. To further investigate these AMOC relationships, we perform a regression analysis and decompose these North Atlantic climate responses into an anthropogenic aerosol-forced component and a subsequent AMOC-related feedback. Similar to previous studies, CMIP6 GHG simulations yield robust AMOC weakening, particularly during the second half of the 20th century. Changes in natural forcings, including solar variability and volcanic aerosols, yield negligible AMOC changes. In contrast, CMIP6 AA simulations yield robust AMOC strengthening (weakening) in response to increasing (decreasing) anthropogenic aerosols. Moreover, the CMIP6 all-forcing AMOC variations and atmospheric circulation responses also occur in the CMIP6 AA simulations, which suggests these are largely driven by changes in anthropogenic aerosol emissions. More specifically, our results suggest that AMOC multi-decadal variability is initiated by North Atlantic aerosol optical thickness perturbations to net surface shortwave radiation and sea surface temperature (and hence sea surface density), which in turn affect sea level pressure gradient and surface wind and – via latent and sensible heat fluxes – sea surface density flux through its thermal component. AMOC-related feedbacks act to reinforce this aerosol-forced AMOC response, largely due to changes in sea surface salinity (and hence sea surface density), with temperature-related (and cloud-related) feedbacks acting to mute the initial response. Although aspects of the CMIP6 all-forcing multi-model mean response resembles observations, notable differences exist. This includes CMIP6 AMOC strengthening from ∼ 1950 to 1990, when the indirect estimates suggest AMOC weakening. The CMIP6 multi-model mean also underestimates the observed increase in North Atlantic ocean heat content, and although the CMIP6 North Atlantic atmospheric circulation responses – particularly the overall patterns – are similar to observations, the simulated responses are weaker than those observed, implying they are only partially externally forced. The possible causes of these differences include internal climate variability, observational uncertainties, and model shortcomings, including excessive aerosol forcing. A handful of CMIP6 realizations yield AMOC evolution since 1900 similar to the indirect observations, implying the inferred AMOC weakening from 1950 to 1990 (and even from 1930 to 1990) may have a significant contribution from internal (i.e., unforced) climate variability. Nonetheless, CMIP6 models yield robust, externally forced AMOC changes, the bulk of which are due to anthropogenic aerosols.

Выполнена оценка возможных изменений среднегодовой приповерхностной температуры воздуха (ПТВ) в Дальневосточном регионе, включающем территорию и окраинные моря России, а также северо-западную часть Тихого океана, до 2099 г., для чего используются осредненные по ансамблю данные 33 моделей проекта CMIP6 (Coupled Model Intercomparison Project Phase 6), полученные в рамках четырех сценариев, отвечающих разным уровням антропогенного радиационного форсинга (слабого, умеренного и значительного). Анализируются различия между осредненными за 30-летние периоды аномалиями ПТВ. Для верификации модельных результатов проанализировано потепление, произошедшее в регионе с 1940–1969 до 1994–2023 гг., для чего использованы данные реанализа ERA5 и эксперимента Historical CMIP6. По обоим видам данных средняя ПТВ в регионе выросла на 1.1 °С: с 1940–1969 к 1994–2023 гг.; это сходство обосновывает оценки будущих изменений ПТВ по моделям CMIP6. Все сценарии SSP (Shared Socio-economic Pathways) будущего радиационного форсинга показывают приблизительно одинаковое повышение ПТВ с 1994–2023 по 2024–2053 гг., оно составляет в среднем по региону 1.2–1.5 °С. К 2070–2099 гг. средняя ПТВ в рассматриваемом регионе возрастет соответственно темпу эмиссии парниковых газов – на 1.7, 2.7, 3.8 и 4.8 °С. Как показывают данные реанализа ERA5, от 1940–1969 к 1994–2023 гг. увеличение ПТВ над морскими акваториями региона происходило весьма неравномерно: наибольшие темпы наблюдались в северной части Охотского моря (до 2 °С и более) и в прибрежных районах северо-западной части Берингова моря (до 1.0–1.2 °С). Увеличение ПТВ ослабевало в направлении с северо-запада на юго-восток, т.е. с удалением от суши, и составило 0.2–0.6 °С в северо-западной части Тихого океана. Картина потепления над морскими акваториями по данным CMIP6 выражена сильнее, чем по данным реанализа ERA5, но при этом качественно им соответствует. An assessment of possible changes in the annual mean surface air temperature (SAT) in the Far East Region (35°–65° N, 130°–180° E) is made from the present to 2099, using ensemble-averaged data from 33 CMIP6 (Coupled Model Intercomparison Project Phase 6) models obtained within the framework of four scenarios corresponding to the weak, moderate, or significant anthropogenic radiative forcing resulting from СО2 emissions. To elucidate long-term climate change, SAT averaged for 30-year periods, namely, 1994–2023, 2024–2053 and 2070–2099 are analyzed. To verify the model results, the warming that occurred in the region from the mid-20th century (1940–1969) to the early 21st century (1994–2023) is analyzed, using ERA5 data with the fine spatial resolution of 0.25°, and CMIP6 data with the coarser resolution, mostly 1.0°–2.0°. According to both data types, the regional SAT increased, on average by 1.1 °C from 1940–1969 to 1994–2023, justifying the use of forecast estimates based on the CMIP6 models in this work. All scenarios of possible radiative forcing show the similar SAT increase from the 1994–2023 to 2024–2053, on average 1.2–1.5 °C. On the contrary, by the 2070–2099, the regional SAT will increase in accordance with the emission rates on average by 1.7, 2.7, 3.8 and 4.8 °C, respectively. As for the Russian Far East land area, ERA5 and CMIP6 show similar spatial warming patterns, with the warming, on average, of 1.2 °C from 1940–1969 to 1994–2023, i.e. higher than that for the entire considered region including marine areas. From 1940–1969 to 1994–2023 negative annual mean SAT changed to positive one in some areas of the Primorsky, Khabarovsky and Kamchatksky provinces, implying the permafrost melting. According to the CMIP6 models, the land warming of 2.0–2.1 °C, 3.0–3.5 °C, 4.7–5.3 °C, and 6.1–6.6 °C is expected by the end of the 21st century for the scenarios with the different levels of radiative forcing. As shown by the ERA5 data, the SAT increase from 1940–1969 to 1994–2023 was very uneven for the marine areas: the highest rates were observed in the northern Okhotsk Sea (up to 2 °C and more) and in the coastal northwestern Bering Sea (up to 1.0–1.2 °C), which can be explained by the ice cover decrease. The SAT increase weakened in the direction from the northwest to southeast, i.e. with the distance from the land, and amounted to only 0.2–0.6 °C in the northwestern Pacific, which can be attributed to the effect of Pacific Decadal Oscillation (PDO). The coastal Okhotsk Sea off the Sakhalin Island is the only area where SAT decreased by 0.2–0.6 °C from 1940–1969 to 1994–2023, which probably can be attributed to the changes in the East Sakhalin Current transporting Amur River water southward along the coast but this suggestion should be verified. The warming pattern over the marine areas according to CMIP6 data qualitatively corresponds to that one based on ERA5 data, keeping in mind the lower resolution of the modeled data. The warming in the Northwest Pacific from the modeled data exceeds that one from ERA5, which can be explained by elimination of the PDO effects when averaging CMIP6 multi-model data.

This work presents updated hydrologic projections for the Upper Colorado River Basin (UCRB) using downscaled (approximately 12 km) General Circulation Model (GCM) output from Coupled Model Intercomparison Project – Phase 5 (CMIP5) with a comparison to CMIP3 GCMs. We use the Soil and Water Assessment Tool model to simulate the impacts of end-of-century climate change on the UCRB using 21 CMIP5 and 18 CMIP3 GCMs, collected into one CMIP5 ensemble and one CMIP3 ensemble, respectively. Previous CMIP3 studies have identified a drier climate for the UCRB because of projected increases in temperature and decreases/little change in precipitation. Hydrologic simulations from CMIP5 inputs suggest wetter conditions than simulations based on CMIP3 inputs, yet drier conditions than the historical climate. Both ensembles lead to timing shifts in peak streamflow during the snowmelt season from changes in snowmelt, but the higher CMIP5 projected precipitation leads to, on average, peak streamflows 200–300 m3 s−1 larger (25–40% difference) than the CMIP3 projections. This difference is largely generated in the northern UCRB region, where CMIP5 simulations project much more significant increases in streamflow than CMIP3. This increase is largely due to an overall larger rise in precipitation in the CMIP5 ensemble (57% of the total UCRB area) compared to the CMIP3 ensemble (5%). Even with projected increases in precipitation, snowmelt is projected to decrease dramatically throughout the UCRB for both ensembles. The increases in precipitation and decreases in snowmelt leads to significant differences in hydrologic flux components between the CMIP3 and CMIP5 ensembles, such as end-of-century rises in soil water content and evapotranspiration in the CMIP5 ensemble compared to the CMIP3 ensemble. The difference between the dry CMIP3 and the somewhat wetter CMIP5 projections may be critical for water management in the already over-allocated UCRB.

Projection of climate extremes in China, an incremental exercise from CMIP5 to CMIP6

Climate change may result in increased variability in rainfall intensity in the future, leading to more frequent flooding and a substantial loss of lives and properties. To mitigate the impact from flooding events, flood control facilities need to be designed and operated more efficiently, which requires a better understanding of the relationship between climate change and flood events. This study proposed a framework combining the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models to assess the impact of climate change on flood events. HEC-HMS is one of the most commonly used hydrologic models in the USA, and CMIP5 provides the latest climate data for potential future climate scenarios. The proposed approach is applied to the Nippersink Creek watershed, which shows that 10-, 25-, 50-, and 100-year precipitations for the low, medium, and high emission scenarios are all greater than the historic observations. The corresponding 10-, 25-, 50-, and 100-year floods are remarkably higher than in the historic observations for the three climate scenarios. The high emission scenario results in dramatically increased flood risks in the future. The case study demonstrates that the framework combining HEC-HMS and CMIP5 is easy to use and efficient for assessing climate change impacts on flood events. It is a valuable tool when complicated and distributed hydrologic modeling is not an option because of time or monetary constraints.

The North Atlantic eddy-driven jet is a dominant feature of extratropical climate and its variability is associated with the large-scale changes in the surface climate of midlatitudes. Variability of this jet is analysed in a set of General Circulation Models (GCMs) from the Coupled Model Inter-comparison Project phase-5 (CMIP5) over the North Atlantic region. The CMIP5 simulations for the 20th century climate (Historical) are compared with the ERA40 reanalysis data. The jet latitude index, wind speed and jet persistence are analysed in order to evaluate 11 CMIP5 GCMs and to compare them with those from CMIP3 integrations. The phase of mean seasonal cycle of jet latitude and wind speed from historical runs of CMIP5 GCMs are comparable to ERA40. The wind speed mean seasonal cycle by CMIP5 GCMs is overestimated in winter months. A positive (negative) jet latitude anomaly in historical simulations relative to ERA40 is observed in summer (winter). The ensemble mean of jet latitude biases in historical simulations of CMIP3 and CMIP5 with respect to ERA40 are -2.43^circ and -1.79^circ respectively. Thus indicating improvements in CMIP5 in comparison to the CMIP3 GCMs. The comparison of historical and future simulations of CMIP5 under RCP4.5 and RCP8.5 for the period 2076–2099, shows positive anomalies in the jet latitude implying a poleward shifted jet. The results from the analysed models offer no specific improvements in simulating the trimodality of the eddy-driven jet.

Ensembles of climate change projections created by general circulation models (GCMs) with high resolution are increasingly needed to develop adaptation strategies for regional climate change. The Meteorological Research Institute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist of members with multiple cumulus convection schemes and different patterns of future sea surface temperature, and are utilized together with their downscaled data; however, the limited size of the high-resolution ensemble may lead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness and effectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2 simulates the present-day regional precipitation around the globe and compare the uncertainty in future precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2 simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models. As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partially overlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions. Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of the uncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble.

Intercomparison of the expected change in the temperature and the precipitation retrieved from CMIP6 and CMIP5 climate projections: A Mediterranean hot spot case, Turkey

General circulation models (GCMs) are indispensable for climate change adaptive study over the Tibetan Plateau (TP), which is the potential trigger and amplifier in global climate fluctuations. With the release of Coupled Model Intercomparison Project Phase 6 (CMIP6), 24 GCMs from CMIP5 and CMIP6 were comparatively evaluated for precipitation and air temperature simulations based on the China Meteorological Forcing Dataset (CMFD). Rank score results showed that CMIP6 models generally performed better than CMIP5 for precipitation and surface air temperature over the TP. According to multimodel ensembles (MMEs) of the optimal GCMs for each climate variable, the overestimation of precipitation was both present in CMIP5 and CMIP6, but the results of CMIP6 MMEs were relatively lower in the mid‐west and northern edge of the TP. Furthermore, CMIP6 offered a better performance of precipitation in summer and autumn. For temperature, CMIP6 MMEs were able to reduce the relatively large cold bias that appeared in CMIP5 MMEs in northwest areas to about 1°C and had a smaller bias in spring and winter. Moreover, the investigation into the simulation effects of precipitation at different elevation zones demonstrated that the improved ability of CMIP6 MMEs to reduce bias was mainly concentrated in the elevation zones of 2,000–3,000 m and over 5,000 m, where the precipitation bias was more than 200%. Additionally, CMIP6 MMEs of temperature were able to reduce the bias to less than 2°C in each elevation zone, with the minimum bias of −0.22°C distributed in the region with altitudes from 3,000 to 4,000 m, while the biases of CMIP5 MMEs in the region of 4,000–5,000 m and over 5,000 m were smaller than those of CMIP6 MMEs. Findings obtained in this study could provide a scientific reference for related climate change research over the TP. GCMs of CMIP6 perform better than those of CMIP5 for precipitation and temperature over the TP. Multimodel ensembles (MMEs) of CMIP6 effectively reduce the overestimation of precipitation from CMIP5 MMEs by 40 mm at the annual scale. Improved ability of CMIP6 MMEs shows a significant elevation dependency, especially in elevation zones of 2,000–3,000 m and over 5,000 m for precipitation.

Knowledge about future global and regional warming is essential for effective adaptation planning and our current temperature projections are based on the output of global climate models (GCMs). Although GCMs agree on the direction of change, there are still significant discrepancies in the magnitude of the projected response1.  Here we develop a novel method2,3 for constraining uncertainty in future regional temperature projections based on the predictions of an observationally trained machine learning algorithm, Ridge-ERA5. Ridge-ERA5 - a Ridge regression model4- learns coefficients to represent observed relationships between daily temperature anomalies and a selection of thermodynamic and dynamical variables in the ECMWF Re-Analysis (ERA) 5 dataset5. Climate-invariance of the Ridge relationships is demonstrated in a perfect model framework: we train a set of 23 Ridge-CMIP models on historical data of the Coupled Model Intercomparison Project (CMIP) phase 66 and evaluate their predictions using future scenario data from the most extreme future emissions pathway, SSP 5-8.5.   Combining the historically constrained Ridge-ERA5 coefficients with normalised inputs from CMIP6 future climate change simulations forms the basis of a new methodology to derive observational constraints on regional climate change. For daily, regional (2°x2°), summer temperatures across the Northern Hemisphere, the Ridge-ERA5 observations-based constraint implies, for example, that a group of higher sensitivity CMIP6 models is inconsistent with observational evidence (including in Eastern, West & Central, and Northern Europe) potentially suggesting that the sensitivity of these models is indeed too high7,8. A key advantage of our new method is the ability to constrain regional projections at very high – daily – temporal resolution which includes extreme events such as heatwaves.    1) Brient, F. (2019) Reducing Uncertainties in Climate Projections with Emergent Constraints: Concepts, Examples and Prospects. Advances in Atmospheric Sciences 2020 37:1, 37(1), pp. 1–15.  2) Ceppi, P. and Nowack, P. (2021) Observational evidence that cloud feedback amplifies global warming. PNAS, 118(30).  3) Nowack, P. et al. An observational constraint on the uncertainty in stratospheric water vapour projections. (in review)  4) Hoerl, A. E. and Kennard, R. W. (1970) Ridge Regression: Applications to Nonorthogonal Problems. Technometrics, 12(1), pp. 69–82.   5) Hersbach, H. et al. (2020) The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, 146(730), pp. 1999–2049.   6) Eyring, V. et al. (2016) Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geoscientific Model Development, 9(5), pp. 1937–1958.   7) Zelinka, M. D. et al. (2020) Causes of Higher Climate Sensitivity in CMIP6 Models. Geophysical Research Letters, 47(1).  8) Zhu, J., Poulsen, C. J. and Otto-Bliesner, B. L. (2020) High climate sensitivity in CMIP6 model not supported by paleoclimate. Nature Climate Change 2020 10:5, 10(5), pp. 378–379. 

This paper examines changes in the strength of the Walker circulation (WC) using the pressure difference between the western and eastern equatorial Pacific. Changes in observations and in 35 climate models from the Coupled Model Intercomparison Project (CMIP) phase 5 (CMIP5) are determined. On the one hand, 78% of the models show a weakening of the WC over the twentieth century, consistent with the observations and previous studies using CMIP phase 3 (CMIP3) models. However, the observations also exhibit a strengthening in the last three decades (i.e., from 1980 to 2012) that is statistically significant at the 95% level. The models, on the other hand, show no consensus on the sign of change, and none of the models shows a statistically significant strengthening over the same period. While the reasons for the inconsistency between models and observations is not fully understood, it is shown that the ability of the models to generate trends as large as the observed from internal variability is reduced because most models have weaker than observed levels of both multidecadal variability and persistence of interannual variability in WC strength. In the twenty-first-century future projections, the WC weakens in 25 out of 35 models, under representative concentration pathway (RCP) 8.5, 9 out of 11 models under RCP6.0, 16 out of 18 models under RCP4.5, and 12 out of 15 models under RCP2.6. The projected decrease is also consistent with results obtained previously using models from CMIP3. However, as the reasons for the inconsistency between modeled and observed trends in the last three decades are not fully understood, confidence in the model projections is reduced.