CMIP5 Models Research Articles

Revisiting the “East African Paradox”: CMIP6 Models Also Struggle to Reproduce Strong Observed Long Rain Drying Trends

Abstract Societies in much of the Horn of Africa are affected by variability in two distinct rainy seasons: the March–May (MAM) “long” rains and the October–December (OND) “short” rains. A recent five-season, La Niña–forced drought has renewed concerns about possible anthropogenic drying trends in the long rains, which had partially recovered after a multidecadal drying trend in the 1980s through the 2000s. Despite observed drying, previous generations of global climate models (GCMs) have consistently projected long-term wetting due to increased greenhouse gas concentrations, an East African “paradox” which complicates the interpretation of East African rainfall projections. We investigate the paradox in new phase 6 of the Coupled Model Intercomparison Project (CMIP6) and seasonal forecast models, leveraging an improved observational record and large ensembles to better differentiate internal and forced trends. We find observed drying trends are at the limits of the GCM spread during the peak paradox period, though the recent recovery is comfortably within the model spread. We find that the apparent paradox is largely removed by prescribing sea surface temperatures (SSTs) and is likely caused by the GCM difficulties in simulating observed tropical Pacific SST trends in recent decades. In line with arguments that these SST trends are at least partially forced anthropogenically, we recommend users of future rainfall projections in East Africa consider the possibility of long-term MAM drying despite GCM wetting and call for future model simulations that better sample the expected spread of SSTs. Significance Statement Societies in the Horn of Africa depend on the March–May “long” rains and the September–December “short” rains to support agricultural and pastoral practices on rain-fed lands. Recent major droughts have raised worries about possible anthropogenically forced drying trends in the long rains through global climate models (GCMs) project wetting. This East African “paradox” complicates long-term climate adaptation planning. We find the paradox continues in the newest generation of GCMs and seasonal forecast models; though a recent recovery in rainfall is well-captured, the strongest observed drying trends are rare in simulations. The paradox likely arises from known GCM biases in the Pacific Ocean interacting with natural variability. We recommend that researchers and policymakers consider possible long-term drying despite GCM projections of wetting.

Observation-Constrained Climate Change in China over 1850–2014 by Aerosols in CMIP6 Models

Abstract Aerosols play a very important role in climate change with large uncertainties. Using the multimodel results from CMIP6, we analyzed the aerosol effective radiative forcing (ERF) and aerosol-induced surface air temperature (SAT) change in China in the present day (PD; 11-yr mean of 2004–14) relative to the preindustrial (PI) time (11-yr mean of 1850–60). With the increase in the anthropogenic emissions, the simulated surface PM2.5 concentration and aerosol optical depth (AOD) averaged over eastern China (EC; 18°–44°N, 103°–122°E) increased by 21.43 ± 7.58 μg m−3 and 0.47 ± 0.33, respectively, from PI to PD. The simulated aerosol ERFs in EC were −4.91 ± 2.56 and −5.35 ± 2.40 W m−2 from equilibrium and transient simulations, respectively. The simulated change in SAT caused by the increases in aerosols was −1.37° ± 0.38°C in EC from PI to PD. The simulated values of equilibrium and transient climate sensitivity to aerosols (CSA; aerosol-induced SAT change per unit aerosol ERF) in EC were 0.236° and 0.222°C (W m−2)−1, respectively. By using the observed AOD from MODIS to constrain aerosol ERF, the constrained aerosol equilibrium and transient ERFs over EC were −4.66 and −4.93 W m−2, respectively, which were smaller in magnitude than the simulated values directly from the models. By using the observed SAT from the Climatic Research Unit temperature version 5 to constrain aerosol-induced cooling, the surface cooling caused by aerosols was magnified to −1.47°C. The adjusted CSA after the constraint was calculated by dividing adjusted aerosol-induced SAT change by adjusted aerosol ERF. Adjusted equilibrium and transient CSA values in EC were 0.32° and 0.34°C (W m−2)−1, respectively.

Northern Hemisphere Atmospheric Blocking in CMIP6 Climate Projections Using a Hybrid Index

Abstract Atmospheric blocking is quantified by a variety of different blocking indices. Here, the index by Davini et al. (DAV12) is modified to a hybrid index by additionally considering the extent of the blocking anticyclone. Applying both indices, the DAV12 and the hybrid index, to the twentieth-century reanalyses, the ERA-20C and the 20CRv3 (hereafter 20CR) ensemble reveals large differences between the reanalyses. The annual blocking frequency increases widespread and significantly in ERA-20C, but only regionally and nonsignificantly in the 20CR ensemble mean. Trend analysis reveals higher reliability of the 20CR ensemble mean than ERA-20C blocking results. Seasonally, the changes in summer blocking are most pronounced and significantly positive around Greenland. The CMIP6-mean historical climate simulation agrees well in magnitude with 20CR during the period 1900–2014, with some underestimation of blocking frequency in the last decades related to inconsistent trends. For a future climate, the average of the CMIP6 models projects a decrease in blocking frequency in major parts of the Northern Hemisphere in summer and in winter. A CMIP6 subset of models which agree best with reanalyses shows a less negative future trend. Because of the mismatch of historical trends, especially in summer, the future projections are uncertain.

Assessment of climate change impact on meteorological variables of Indravati River Basin using SDSM and CMIP6 models.

Climate change, one of the most pressing issues of the twenty-first century, threatens the long-term stability and short-term variability of water resources. Variations in precipitation and temperature will influence runoff and water availability, creating significant challenges as demand for potable water increases. This study addresses a critical literature gap by employing the Statistical Downscaling Model (SDSM) to downscale Global Climate Model (GCM) outputs for the Indravati River Basin, India. Maximum temperature (Tmax), minimum temperature (Tmin), and precipitation (PCP) were statistically downscaled, improving the spatial resolution of coarse GCM data. The model established strong predictor-predictand relationships, offering enhanced local-scale climate projections for the basin. This work provides critical insights into regional climate change impacts in a previously underexplored area. The study projected the meteorological variables (Tmax, Tmin, and PCP) for Chindnar, Jagdalpur, and Pathagudem stations using four GCMs, namely CanESM5, MPI-ESM1-2-HR, EC-Earth3, and NorESM2-LM for the baseline period (1990-2014). The Correlation Coefficient-values (R-values) range from 0.75 to 0.91 for maximum temperature, 0.85 to 0.96 for minimum temperature, and 0.71 to 0.83 for precipitation were achieved using SDSM. The best-performed MPI-ESM1-2-HR model was used to project maximum temperature, minimum temperature, and precipitation for 2024-2054 (2040s) and 2055-2085 (2070s) under SSP4.5 and SSP8.5 scenarios using SDSM. The downscaled results revealed significant shifts in meteorological patterns, highlighting the basin's sensitivity to different socio-economic pathways and future climate conditions. The percentage monthly, seasonal, and annual variations of Tmax, Tmin, and PCP were analysed based on each scenario and time period to suggest remedial measures for future floods and droughts.

Assessing the forecast capability of the El Niño–Southern Oscillation with a one-year lead-time using the Global Atmospheric Oscillation based on the results of CMIP6 models

Assessing the forecast capability of the El Niño–Southern Oscillation with a one-year lead-time using the Global Atmospheric Oscillation based on the results of CMIP6 models

CMIP6 Models Underestimate Arctic Sea Ice Loss during the Early Twentieth-Century Warming, despite Simulating Large Low-Frequency Sea Ice Variability

Abstract The variability of Arctic sea ice extent (SIE) on interannual and multidecadal time scales is examined in 29 models with historical forcing participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) and in twentieth-century sea ice reconstructions. Results show that during the historical period with low external forcing (1850–1919), CMIP6 models display relatively good agreement in their representation of interannual sea ice variability (IVSIE) but exhibit pronounced intermodel spread in multidecadal sea ice variability (MVSIE), which is overestimated with respect to sea ice reconstructions and is dominated by model uncertainty in sea ice simulation in the subpolar North Atlantic. We find that this is associated with differences in models’ sensitivity to Northern Hemispheric sea surface temperatures (SSTs). Additionally, we show that while CMIP6 models are generally capable of simulating multidecadal changes in Arctic sea ice from the mid-twentieth century to present day, they tend to underestimate the observed sea ice decline during the early twentieth-century warming (ETCW; 1915–45). These results suggest the need for an improved characterization of the sea ice response to multidecadal climate variability in order to address the sources of model bias and reduce the uncertainty in future projections arising from intermodel spread. Significance Statement The credibility of Arctic sea ice predictions depends on whether climate models are capable of reproducing changes in the past climate, including patterns of sea ice variability which can mask or amplify the response to global warming. This study aims to better understand how latest-generation global climate models simulate interannual and multidecadal variability of Arctic sea ice relative to available observations. We find that models differ in their representation of multidecadal sea ice variability, which is overall larger than in observations. Additionally, models underestimate the sea ice decline during the period of observed warming between 1915 and 1945. Our results suggest that, to achieve better predictions of Arctic sea ice, the realism of low-frequency sea ice variability in models should be improved.

Cross-Seasonal Effect on Tropical Pacific Precipitation: Implication of South Pacific Quadrupole Simulation in CMIP6

Abstract The tropical Pacific convergence zone plays a crucial role in the global climate system. Previous research studies emphasized the cross-seasonal influence of the South Pacific quadrupole (SPQ) mode on the tropical Pacific climate. This study assesses the relationship between austral summer SPQ and austral winter tropical precipitation in phase 6 of the Coupled Model Intercomparison Project (CMIP6) models. The analysis emphasizes the historical experiments conducted within this time frame, spanning from 1979 to 2014. Our findings reveal that the SPQ is accurately represented in all CMIP6 models, but the connection between SPQ and precipitation is inadequately simulated in most models. To investigate the reasons behind these intermodel differences in reproducing SPQ-related processes, we categorize models into two groups. The comparisons demonstrate that the fidelity of model simulations in replicating the SPQ–tropical precipitation relationship hinges significantly on their capacity to reproduce the positive wind–evaporation–sea surface temperature (WES; SST) feedback over both the southwestern Pacific (25°–10°S; 150°E–160°W) and the southeastern Pacific (30°–10°S; 140°–80°W). This positive WES feedback propagates the SPQ signal into the tropics, intensifying the meridional gradient of SST anomaly in the tropical western-central Pacific, which consequently amplifies convection and rainfall in that area. In the group of models that failed to simulate this relationship accurately, the weakened WES feedback can be traced back to biases in wind speed and its variation. Furthermore, this WES feedback establishes a connection between SPQ and El Niño–Southern Oscillation (ENSO). A better rendition of the SPQ–tropical rainfall connection tends to result in a better simulation of the onset of SPQ-related ENSO events. As a result, this study advances our comprehension of extratropical impacts on the tropics, with the potential to enhance the accuracy of tropical climate simulation and prediction. Significance Statement Tropical rainfall plays an important role in the global climate system. Beyond the well-known influence of El Niño–Southern Oscillation (ENSO) on the tropical rainfall, the sea surface temperature (SST) anomaly in the South Pacific has a cross-seasonal impact on the precipitation over the tropical Pacific via air–sea coupled processes. Such SST anomaly pattern shows a quadrupole structure in the extratropical South Pacific, known as the South Pacific quadrupole (SPQ) mode. However, the relationship between SPQ and tropical precipitation remains poorly simulated in most state-of-the-art climate models. One primary reason for this gap between observed and simulated relationships is the underestimation of wind speed and its variation over the south tropical Pacific in these models. This limitation undermines their ability to accurately represent the air–sea interactions that drive tropical precipitation patterns, leading to inaccuracies in simulations. Our study aims to bridge this knowledge gap by enhancing our understanding of the extratropical effects on the tropical Pacific. By exploring the mechanisms underlying the SPQ–precipitation connection, we expect to improve the simulation and prediction capabilities of tropical climate models, thereby enhancing our ability to forecast and adapt to future climatic changes.

Process-Based Evaluation of Intraseasonal Oceanic Kelvin Waves in the Pacific Ocean in CMIP6 Models

Abstract Oceanic intraseasonal Kelvin waves (KWs) help modulate upper-ocean thermal characteristics, providing feedbacks to important coupled air–sea phenomena in the tropics. The recent availability of daily thermocline depth fields from several phase 6 of the Coupled Model Intercomparison Project (CMIP6) models makes it possible to evaluate the performance of KWs and identify potential sources of bias. Most models fail to simulate a realistic spatial distribution of KW variability. Models simulate a large variability of KWs in the western or eastern Pacific rather than in the central Pacific as observed. The modeled KWs propagate slowly (about 1.5 m s−1) compared to observations (about 2.5 m s−1). This slow propagation is also identified in wavenumber–frequency spectra for KWs and meridional KW structures, which is more consistent with a second baroclinic mode structure in models compared to the first baroclinic mode structure in observations. An analysis of the relative contributions of the vertical wavenumber and background ocean stability to KW phase speeds indicates that the high vertical wavenumber bias in models contributes most to the slow propagation, in which the higher-than-observed vertical wavenumbers imply the biased incorporation of higher baroclinic modes in the model KW structure. This finding is further supported by the results of vertical mode decomposition that incorporates background density profiles. These results indicate that a realistic representation of the KW vertical structure is essential to produce realistic KW propagations in models. Significance Statement Oceanic intraseasonal Kelvin waves (KWs) play a significant role in regulating the heat content and temperature of the ocean, which provides feedbacks to coupled ocean–atmosphere phenomena in the tropics such as El Niño–Southern Oscillation (ENSO). Consequently, identifying the biases in KWs and the potential sources of those biases in state-of-the-art models is essential to improve simulations of ENSO and its diversity and advance forecasts of weather extremes around the globe induced by ENSO. We find that KWs in the models propagate slower than observations, mainly due to biases in their vertical structure, with secondary effects due to biases in model ocean stability. These issues may be potential sources for current imperfect ENSO model simulations and predictions.

Indian Ocean Intermediate Water Masses and Their Simulations by CMIP6 Models

Abstract Water masses are carriers of anthropogenic fingerprints in the ocean interior, with their property changes manifesting oceanic thermodynamic responses to climate change. Yet, delimiting ocean water masses remains challenging in either observational atlas or climate models. This study analyzes the distribution of Indian Ocean seawater in the density–spicity space and uses volumetric maxima and minima between σ = 27.1 and 27.4 kg m−3 to track the cores and boundaries of intermediate water masses, respectively. In addition to the well-known Antarctic Intermediate Water (AAIW) and Red Sea–Persian Gulf Intermediate Water (RS-PGIW), two other water masses are identified by the new approach. One is the Indian-AAIW (I-AAIW), as a mixture of the AAIW and the Indonesian Throughflow water, existing in the South Equatorial Current and the Agulhas Current system. The other [equatorial Indian Intermediate Water (EIIW)] exits in the equatorial Indian Ocean and Bay of Bengal, sourced from the RS-PGIW and overlying fresh waters. These waters are corroborated by nutrient and dissolved oxygen data. Around half (26 out of 51) of phase 6 of the Coupled Model Intercomparison Project (CMIP6) models can reasonably simulate these intermediate water masses. Compared with the observed water masses, the intermediate water masses in models are of a smaller thickness and the RS-PGIW is colder and fresher. The former arises from a warm bias in the thermocline, whereas the latter is likely linked to insufficient ventilation in the Red Sea and Persian Gulf in models owing to coarse grid resolution and a surface cold bias. Significance Statement Oceanic water masses are important for understanding climate change. Yet, the definition of water masses is controversial. Here, we use volumetric minima in the density–spicity space to track the boundaries of intermediate (27.1–27.4 kg m−3) water masses in the Indian Ocean. We identify four water masses: AAIW, I-AAIW, EIIW, and RS-PGIW. We delineate the geographical location of each water mass, including two newly identified water masses (I-AAIW and EIIW). We also examined the performance of 51 CMIP6 models in simulating these water masses and found that 26 models can reasonably simulate the distribution of these water masses. There are two systematic model biases emerging from these models. The intermediate water masses in models are of a thinner thickness arising from a warm bias in the thermocline, and the RS-PGIW is colder and fresher owing to insufficient ventilation in the Red Sea and Persian Gulf. These results provide a useful benchmark for understanding water masses in a changing climate and constraining climate models.

Reconstruction of Historical Site-Scale Dust Optical Depth (DOD) Time Series from Surface Dust Records and Satellite Retrievals in Northern China: Application to the Evaluation of DOD in CMIP6 Historical Simulations

Abstract The biases generated by state-of-the-art climate models in simulating dust optical depth (DOD) remain to be detailed. Here, a site-scale DOD dataset in March–August over northern China (NC) during 1980–2001 was reconstructed using the empirical relationship between MODIS-retrieved DOD and dust-event frequencies during 2001–21. Then, through the combined use of MODIS-based and reconstructed DOD, we evaluated the reproducibility of DOD from 10 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the historical period (1980–2001 and 2002–14) and under different Shared Socioeconomic Pathways (SSPs) during 2015–21. The results demonstrate that CMIP6 models and multimodel ensemble mean (MEM) are capable of capturing the spatial pattern of DOD, but with considerable uncertainty and intermodel variability in magnitude. Regionally averaged DOD is underestimated by 56.09% during 1980–2001 and overestimated by 30.97% during 2002–14 in MEM over NC. Simultaneously, the intermodel standard deviations are greater than MEM during 2002–14, suggesting large discrepancies among individual models. Very few models accurately capture the trends in DOD, which can mainly be attributed to the different trends in simulated wind speed (WS), soil moisture, and vegetation cover, and their contributions to dust evolution. Under four SSPs, despite the best correlation between SSP1-2.6-modeled and MODIS DOD over Gobi Desert (GD), overestimation of DOD is still observed. More models under SSP1-2.6 capture the positive DOD trend, mainly attributable to positive changes in simulated WS over GD.

Equatorial Pacific Cold Tongue Bias Degrades Simulation of ENSO Asymmetry due to Underestimation of Strong Eastern Pacific El Niños

Abstract El Niño–Southern Oscillation (ENSO) exhibits a considerable asymmetry in sea surface temperature anomalies (SSTa), as El Niño events tend to be stronger and centered further east than La Niña events. Here, we analyze ENSO asymmetry in observations and preindustrial control integrations of 32 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Observations indicate a significant link between strong eastern Pacific (EP) El Niño events and the ENSO amplitude and asymmetry. The large CMIP6 database confirms this strong link. Most CMIP6 models suffer from an equatorial Pacific cold SST bias. This cold tongue bias hinders the southward migration of the ITCZ toward the equator over the eastern equatorial Pacific, which is characteristic of strong EP El Niño events in observations. Therefore, many models underestimate the eastern equatorial Pacific rainfall anomalies and simulate a wind stress feedback over the western Pacific that is too weak and too far west. As a result, the cold tongue bias exerts a strong control on the climate models’ ability to generate strong EP El Niño events and therefore on the ENSO overall amplitude and asymmetry. We discuss the relevance of these results in view of a potential increase of strong EP El Niño events under global warming. Significance Statement El Niño and La Niña are asymmetric, as El Niño events tend to be stronger and further east than La Niña events. Due to an equatorial Pacific cold tongue bias with too cold SSTs, the simulation of ENSO asymmetry is degraded in many climate models participating in CMIP6. Here, we show how the cold tongue bias influences ENSO asymmetry. The cold bias hampers the simulation of strong eastern Pacific El Niños by making it more difficult for SST to exceed the threshold for deep atmospheric convection over the eastern equatorial Pacific. Recent research indicates that climate models with a realistic ENSO asymmetry agree on the projected ENSO under global warming. The results of this study suggest that reducing the cold tongue bias has the potential to enhancing the reliability of future ENSO projections.

Constrained CMIP6 projections indicate higher risks of future water shortages in Australia

Study RegionAustralia. Study FocusObservations and simulations both indicate a negative feedback between precipitation and temperature in arid environments, where low precipitation reduces the evaporative cooling effect, resulting in higher temperature. The negative sensitivity of temperature to precipitation is a key driver of the Australian land water cycle and an important source of uncertainty in climate model projections. Currently, the spread of projected future sensitivities of temperature to changes in precipitation remains substantial across models. To reduce this spread, we demonstrated the existence of a robust emergent relationship between historical wet season temperature sensitivity and future annual temperature sensitivity across all 36 CMIP6 (under emission scenarios SSP126 and SSP245) and 36 CMIP5 (under RCP26, RCP45 and RCP60) models. Combined with observations, we reduced the uncertainty of future temperature sensitivity projections. New Hydrological Insights for the RegionThe constrained results show that the uncertainties of future sensitivities of temperature to precipitation changes are reduced by 27.0–46.8 % (CMIP6) and 11.2–31.1 % (CMIP5). We reveal that the raw CMIP5 and CMIP6 projections both largely overestimate the future negative sensitivity of temperature to precipitation in Australia. These overestimated evaporative cooling effects suggest that future decreases in soil water and increases in drought frequency are also highly underestimated. In particular, the constrained results show an increase in total evaporation across Australia, which contrasts with the decreasing trend in the raw CMIP6 outputs.

Simulation of modern and future climate by INM-CM6M

Abstract The paper considers the results of climate change modelling for 1850–2100 using the INM-CM6M climate model of the Marchuk Institute of Numerical Mathematics of the Russian Academy of Sciences. The calculations were performed according to the CMIP6 protocol for modelling the present-day climate for the period from 1850 to 2014 and the IPCC scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5 of anthropogenic forcing changes in 2015–2100. We analyse changes in such characteristics of the climate system as global mean near-surface temperature, spatial distributions of near-surface temperature, precipitation, sea level pressure, and radiative forcing characteristics in comparison with observational data and results obtained with the previous (INM-CM5) version of the model. We conclude that the new version of the model is superior to the previous one in terms of the quality of simulation of the observed climate and its changes in 1960–2022. The amplitude of global warming predicted by the INM-CM6M for moderate IPCC scenarios is close to the average value of the CMIP6 model ensemble, and for severe scenarios it is closer to the upper limit of its range. For the SSP3-7.0 and SSP5-8.5 scenarios, INM-CM6M predicts a complete loss of Arctic sea ice in summer. As the rate of global warming increases, so does the amplitude and duration of extreme weather and climate events.

Sea level rise and coastal flooding risks in the Gulf of Guinea

The Gulf of Guinea (GoG) is highly vulnerable to sea level rise, with projections indicating a significant increase in permanently inundated land by 2100, ranging from 1,458.1 to 4,331.7 km2. This study evaluates the severity of potential coastal inundation in the GoG by comparing sea level rise projections from eight reliable CMIP6 models with historical sea surface height (SSH) data from 1993 to 2015 and current onshore topography. Eight model simulations were selected based on their accuracy in reproducing sea level variability in the Tropical Atlantic and the GoG, and their consistency in reflecting the one-month connection lag between equatorial-driven waves and Kelvin Coastal Trapped Waves (CTWs) along the GoG, critical for predicting regional ocean dynamics. Our findings indicate that this connection lag will remain consistent over time. Under high-emission scenarios, up to 95% of coastal areas could be inundated, potentially displacing 2 million people posing a socio-economic shock, given the region’s low GDP and heavy reliance on fisheries. The loss of cultural heritage and livelihoods further compounds the challenges. These findings emphasize the urgent need for targeted adaptation strategies and robust early warning systems, in line with the UN’s Sustainable Development Goals (SDGs), particularly SDG 13 (Climate Action) and SDG 14 (Life Below Water). This study offers a precise and regionally relevant assessment of future risks, providing a foundation for informed policy interventions to mitigate the impacts of climate change and protect vulnerable communities in the GoG.

Advancing effective radius parameterizations in climate models: insights from fundamental theoretical studies and CMIP6 model

Advancing effective radius parameterizations in climate models: insights from fundamental theoretical studies and CMIP6 model

Simulation and projection of photovoltaic energy potential over a tropical region using CMIP6 models

The study examines the potential impact of climate change on photovoltaic energy (PV) in Nigeria. Solar radiation and temperature datasets from 13 regional climate models (CMIP6) for 2015–2099 were used to evaluate the photovoltaic energy under moderate (SSP245) and high (SSP585) emission scenarios for near-future (2023–2053), mid-future (2054–2084), and far-future (2084–2099) periods. The precision of the models for the simulation of PV energy was validated with MERRA-2 reference data using the compromise programming index (CPI). Models with the lowest CPI were selected for regional PV energy projections. The findings showed varying numbers of increase and decrease projected changes across the four regions under SSP245 and SSP585 scenarios for the near, mid and far future timescales. Specifically, in the SSP245 scenario, the model with lowest CPI was the CMCC-CESM2 model, it projected a decrease in PV energy in the Sahel (−2.30), an increase in the Guinea Savannah (+2.80), the Rainforest (+1.20) and the coastal region (+4.80) for the far future period (2085–2099). In the SSP585 scenario, the AWI-CM-1.1-MR model projected a decrease in the Sahel region (−4.60), while the MPI-ESM1-2-LR model projected an increase in the Guinea Savannah region (+1.80), and the ACCESS-CM2 model projected an increase in the Rainforest (+10.20) and Coastal regions (+13.20) for the far-future. All values in the parentheses are measured in watts-hour per square-meters. The projected changes in PV energy revealed the need for a regional-specific approach to the planning and implementation of energy transition mix in Nigeria.

Permafrost Thawing and Estimates of Vulnerable Carbon in the Northern High Latitude

AbstractThe degradation of permafrost in the Northern Hemisphere is expected to persist and potentially worsen as the climate continues to warm. Thawing permafrost results in the decomposition of organic matter frozen in the ground, which stores large amounts of soil organic carbon (SOC), leading to carbon being emitted into the atmosphere in the form of carbon dioxide and methane. This process could potentially contribute to positive feedback between global climate change and permafrost carbon emissions. Accurate projections of permafrost thawing are key to improving our estimates of the global carbon budget and future climate change. Using data from the latest generation of climate models (CMIP6), this paper explores the challenges involved in assessing the annual active layer thickness (ALT), defined as the maximum annual thaw depth of permafrost, and estimated carbon released under various Shared Socioeconomic Pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5). We find that the ALT estimates derived from CMIP6 model soil temperatures show significant deviations from the observed ALT values. This could lead to inconsistent estimates of carbon release under climate change. We propose a simplified approach to improve the estimate of the changes in ALT under future climate projections. These predicted ALT changes, combined with present-day observations, are used to estimate vulnerable carbon under future climate projections. CMIP6 models project ALT changes of 0.1–0.3 m per degree rise in local temperature, resulting in an average deepening of approx. 1.2–2.1 m in the northern high latitudes under different scenarios. With increasing temperatures, permafrost thawing starts in Southern Siberia, Northern Canada, and Alaska, progressively extending towards the North Pole by the end of the century under high emissions scenarios (SSP5-8.5). Using projections of ALT changes and vertically resolved SOC data, we estimate the ensemble mean of decomposable carbon stocks in thawed permafrost to be approximately 115 GtC (gigatons of carbon in the form of CO2 and CH4) under SSP1-2.6, 180 GtC under SSP2-4.5, 260 GtC under SSP3-7.0, and 300 GtC under SSP5-8.5 by the end of the century.

Investigating the “Too Bright” Issue Pertaining to Non‐PBL Clouds Over the South Pacific Trade‐Wind Region in CMIP6 Global Climate Models

AbstractThis paper examines the “too bright” issue pertaining to non‐planetary boundary layer (PBL) clouds over the South Pacific trade‐wind region and its potential link to the falling ice radiative effects (FIREs). We run sensitivity experiments with CESM2‐CAM6 (CESM2) global climate model with FIREs on (SON) and off (NOS). The model exhibits more in‐cloud liquid water content (CLWC) and droplet above the PBL in NOS, leading to larger shortwave (SW) reflectivity at the top of the atmosphere than in SON over the trade wind regions. CMIP6 models are divided into three subsets: separately calculates the radiative effects of cloud ice and falling ice (SON2), combined (SON1) and without falling ice (NOS). SON2 models exhibit improved CLWC and SW reflectivity similar to CESM2‐SON, while NOS and SON1 models are akin to CESM2‐NOS owing to weaker surface wind stress and warmer ocean surface, caused by the lack of FIREs over the convective zones.

Evaluation of Equatorial Westerly Wind Events in the Pacific Ocean in CMIP6 Models

Abstract Westerly wind events (WWEs) are anomalously strong, long-lasting westerlies over the Indian or Pacific Oceans that are capable of forcing oceanic wave modes, which in turn can impact the evolution of coupled ocean–atmosphere phenomena such as El Niño–Southern Oscillation (ENSO). This work examines the fidelity of equatorial WWEs over the Pacific Ocean in 30 CMIP6 historical simulations against observations. WWEs are identified using equatorially averaged zonal wind stress anomaly duration, zonal extent, and intensity criteria. Most simulations correctly place the majority of WWEs over the west Pacific although they are skewed westward and generally occur less frequently compared to observations. Simulated WWEs tend to be weaker than observations for a given duration and zonal extent with several models having shorter durations and zonal extents than observations. Biases in simulated WWEs are associated with biases in Madden–Julian oscillation (MJO) and convectively coupled Rossby wave (CRW) variability. Models that underpredict WWE forcing in the west Pacific also severely underpredict MJO and CRW variance. Further, the multimodel mean shows a smaller fraction of WWEs associated with both the MJO and CRW than observations.

Assessing CMIP Models’ Ability to Detect Observed Surface Warming Signals Related to Climate Change

Abstract Assessing if CMIP models can detect observed climate change signals is crucial for evaluating their realism and strengthening confidence in future projections. These signals can only be compared once distinguishable from the background climate “noise.” Built for impact studies but not detection purpose, the time of emergence (ToE) estimates when this occurs using a fixed signal-to-noise ratio. To more accurately assess models’ ability to reproduce observed climate change signals, we introduce the time of detection (ToD), which employs a statistical significance test of the nonlinear trend, accounting for the time series length. ToD and ToE are computed for sea surface temperature (SST) and relative SST (RSST) across four observational and 69 CMIP5/6 historical and shared socioeconomic pathways 5–8.5 (SSP5–8.5) scenario datasets. ToD precedes ToE and is less sensitive to the chosen threshold. While already detectable in most of the tropics, SST warming in the equatorial Pacific is detectable in ∼90% of models but only one out of four observational datasets, due to a stronger modeled warming. Because RSST signals are weaker than SST signals, they do not emerge anywhere but are detectable in two regions. The enhanced warming in the western Indian Ocean, detected in ∼45% of models, is not robust in observations when excluding pre-1960 dubious data. Conversely, the subdued southeast Pacific warming detected in observations and ∼80% of models is robust and consistent with a poleward extension of the Southern Hemisphere Hadley cell. The ToD’s ability to detect subtle signals allows for assessing the realism of the CMIP warming pattern in this region. Significance Statement Recent studies emphasize the importance of the climate change warming pattern and its apparent discrepancy between climate models and observations in the equatorial Pacific. We argue that our newly proposed time of detection (ToD) is better suited than the well-known time of emergence (ToE) for investigating where modeled and observed warming patterns can be confidently compared. In agreement with previous studies, our analysis indicates model–observations disagreement over the equatorial Pacific. Using ToD instead of ToE allows for detecting a robust subdued warming (i.e., weaker than the tropical average) in the southeast Pacific and therefore confirms the realism of the CMIP warming pattern in this region.