Climate Model Evaluation Research Articles

The Intrinsic 150‐Day Periodicity of the Southern Hemisphere Extratropical Large‐Scale Atmospheric Circulation

AbstractThe variability of the Southern Hemisphere (SH) extratropical large‐scale circulation is dominated by the Southern Annular Mode (SAM), whose timescale is extensively used as a key metric in evaluating state‐of‐the‐art climate models. Past observational and theoretical studies suggest that the SAM lacks any internally generated (intrinsic) periodicity. Here, we show, using observations and a climate model hierarchy, that the SAM has an intrinsic 150‐day periodicity. This periodicity is robustly detectable in the power spectra and principal oscillation patterns (aka dynamical mode decomposition) of the zonal‐mean circulation, and in hemispheric‐scale precipitation and ocean surface wind stress. The 150‐day period is consistent with the predictions of a new reduced‐order model for the SAM, which suggests that this periodicity is associated with a complex interaction of turbulent eddies and zonal wind anomalies, as the latter propagate from low to high latitudes. These findings present a rare example of periodic oscillations arising from the internal dynamics of the extratropical turbulent circulations. Based on these findings, we further propose a new metric for evaluating climate models, and show that some of the previously reported shortcomings and improvements in simulating SAM's variability connect to the models' ability in reproducing this periodicity. We argue that this periodicity should be considered in evaluating climate models and understanding the past, current, and projected Southern Hemisphere climate variability.

Model‐Data Comparison of Antarctic Winter Sea‐Ice Extent and Southern Ocean Sea‐Surface Temperatures During Marine Isotope Stage 5e

AbstractMarine Isotope Stage (MIS) 5e (130–116 ka) represents a “laboratory” for evaluating climate model performance under warmer‐than‐present conditions. Climate model simulations for MIS 5e have previously failed to produce Southern Ocean (SO) sea‐surface temperatures (SSTs) and sea‐ice extent reconstructed from marine sediment core proxy records. Here we compare state of the art HadGEM3 and HadCM3 simulations of Peak MIS 5e SO summer SSTs and September sea‐ice concentrations with the latest marine sediment core proxy data. The model outputs and proxy records show the least consistency in the regions located near the present‐day SO gyre boundaries, implying the possibility that model simulations are currently unable to fully realize changes in gyre extent and position during MIS 5e. Including Heinrich 11 meltwater forcing in Peak MIS 5e climate simulations improves the likeness to proxy data but it is clear that longer (3–4 ka) run times are required to fully test the consistency between models and data.

Various ways of using empirical orthogonal functions for climate model evaluation

Abstract. We present a framework for evaluating multi-model ensembles based on common empirical orthogonal functions (common EOFs) that emphasize salient features connected to spatio-temporal covariance structures embedded in large climate data volumes. This framework enables the extraction of the most pronounced spatial patterns of coherent variability within the joint dataset and provides a set of weights for each model in terms of the principal components which refer to exactly the same set of spatial patterns of covariance. In other words, common EOFs provide a means for extracting information from large volumes of data. Moreover, they can provide an objective basis for evaluation that can be used to accentuate ensembles more than traditional methods for evaluation, which tend to focus on individual models. Our demonstration of the capability of common EOFs reveals a statistically significant improvement of the sixth generation of the World Climate Research Programme (WCRP) Climate Model Intercomparison Project (CMIP6) simulations in comparison to the previous generation (CMIP5) in terms of their ability to reproduce the mean seasonal cycle in air surface temperature, precipitation, and mean sea level pressure over the Nordic countries. The leading common EOF principal component for annually or seasonally aggregated temperature, precipitation, and pressure statistics suggests that their simulated interannual variability is generally consistent with that seen in the ERA5 reanalysis. We also demonstrate how common EOFs can be used to analyse whether CMIP ensembles reproduce the observed historical trends over the historical period 1959–2021, and the results suggest that the trend statistics provided by both CMIP5 RCP4.5 and CMIP6 SSP245 are consistent with observed trends. An interesting finding is also that the leading common EOF principal component for annually or seasonally aggregated statistics seems to be approximately normally distributed, which is useful information about the multi-model ensemble data.

Evaluation of ERA5-Simulated Temperature and Its Extremes for Australia

Atmospheric reanalysis products offer high-resolution and long-term gridded datasets that can often be used as an alternative or a supplement to observational data. Although more accessible than typical observational data and deemed fit for climate change studies, reanalysis data can show biases resulting from data assimilation approaches. Thus, a thorough evaluation of the reanalysis product over the region and metric of study is critical. Here, we evaluate the performance of the latest generation of ECMWF reanalysis, ERA5, in simulating mean and extreme temperatures over Australia for 1979–2020 versus high-quality gridded observations. We find ERA5 generally simulates maximum and minimum temperatures reasonably well (mean bias ~1.5 °C), even though it underestimates/overestimates the daily maximum/minimum temperatures, leading to a cold bias for Tmax and a warm bias for Tmin. ERA5 also underestimates the decadal warming trend in both Tmax and Tmin compared to the observations. Furthermore, ERA5 struggles to simulate the temporal variability of Tmin, leading to a markedly worse skill in Tmin than Tmax. In terms of extreme indices, ERA5 is skilled at capturing the spatial and temporal patterns and trends of extremes, albeit with the presence of biases in each index. This can partially be attributed to the warm bias in the minimum temperature. Overall, ERA5 captures the mean and extreme temperature indices over the Australian continent reasonably well, warranting its potential to supplement observations in aiding climate change-related studies, downscaling for boundary conditions, and climate model evaluation.

Performance evaluation and ranking of CMIP6 global climate models over Vietnam

Abstract This study comprehensively assesses the performance of 29 Coupled Model Intercomparison Project Phase 6 (CMIP6) Global Climate Models (GCMs) and their ensemble mean (ENS_MEAN) over Vietnam. The spatiotemporal variability of near-surface temperature and precipitation is thoroughly evaluated for the 30-year historical period of 1985–2014. Results show that the models can reasonably reproduce the observational annual cycles and spatial distribution of temperature and precipitation, though their performances vary across the seven climatic sub-regions of Vietnam. Due to their coarse resolutions, the models produce warm biases over certain highland and mountainous areas, and many of them cannot reproduce the rainy season shift from summer in most sub-regions toward the end of the year in Central Vietnam. Finally, these results are summarized, and a performance score is assigned to each model, inferring a ranking. The top three models are EC-Earth3-Veg, EC-Earth3, and HadGEM3-GC31-MM. Although created by simply averaging all the models, ENS_MEAN can compete with the best GCMs and ranks 4th overall. In the same model family, the higher-resolution experiment exhibits a higher ranking than the lower-resolution one. The results of this study can provide a reference for the choice of appropriate CMIP6 GCMs in the upcoming downscaling activities in Vietnam.

Last glacial maximum cooling of 9 °C in continental Europe from a 40 kyr-long noble gas paleothermometry record

The Last Glacial Maximum (LGM; ∼26–18 kyr ago) is a time interval of great climatic interest characterized by substantial global cooling driven by radiative forcings and feedbacks associated with orbital changes, lower atmospheric CO2, and large ice sheets. However, reliable proxies of continental paleotemperatures are scarce and often qualitative, which has limited our understanding of the spatial structure of past climate changes. Here, we present a quantitative noble gas temperature (NGT) record of the last ∼40 kyr from the Albian aquifer in Eastern Paris Basin (France, ∼48°N). Our NGT data indicate that the mean annual surface temperature was ∼5 °C during the Marine Isotope Stage 3 (MIS3; ∼40–30 kyr ago), before cooling to ∼2 °C during the LGM, and warming to ∼11 °C in the Holocene, which closely matches modern ground surface temperatures in Eastern France. Combined with water stable isotope analyses, NGT data indicate δD/NGT and δ18O/NGT transfer functions of +1.6 ± 0.4‰/°C and +0.18 ± 0.04‰/°C, respectively. Our noble-gas derived LGM cooling of ∼9 °C (relative to the Holocene) is consistent with previous studies of noble gas paleothermometry in European groundwaters but larger than the low-to-mid latitude estimate of 5.8 ± 0.6 °C derived from a compilation of noble gas records, which supports the notion that continental LGM cooling was more extreme at higher latitudes. While an LGM cooling of ∼9 °C in Eastern France appears compatible with recent data assimilation studies, this value is greater than most estimates from current-generation climate model simulations of the LGM. Comparing our estimate for the temperature in Eastern France during MIS3 (6.4 ± 0.5 °C) with GCM outputs presents a promising avenue to further evaluate climate model simulations and constrain European climate evolution over the last glacial cycle.

Simulating satellite urban land surface temperatures: sensitivity to sensor view angle and assumed landscape complexity

Urban land surface temperature (LST) from satellite earth observation (EO) varies with sensor view angle. Where these variations are not accounted for, urban LST products are inconsistent through time, limiting their use in urban weather and climate model evaluations and process studies (e.g. urban heat island, building energy balance, human thermal comfort). Obstacle-resolving numerical models (ORM) of urban form and radiation exchanges are being used to: (a) understand relations between EO view angle, the 3-dimensional urban surface, and the surface temperatures from urban land surface models, and (b) evaluate parameterisations (parametric models) that aim to account for LST angular effects for the next operational satellite products. Most ORM are limited to simplified buildings (e.g. cuboids) and surface temperatures by lack of datasets. Novelly, we use both a realistic urban form model and observed surface temperatures to assess the impact of simplifying the urban form and temperature on the modelled LST anisotropy. We test various sets of assumptions in central London by combining ground-based thermal camera observations and the state-of-the-art Discrete Anisotropic Radiative Transfer (DART) model. The high resolution realistic model (< 1 m) includes thermal (surface temperatures varying by sun-surface geometry, shadow history and materials every 30 min) and geometry (sloped roofs, chimneys and vegetation) data. These data are used to simulate brightness temperatures of EO pixels to quantify LST view angle variations.During daytime, a change of view angle of 47° off-nadir corresponds to a difference in LST of up to 5.1 K for the realistic building model. The intermediate-complexity landscape (easily obtainable building geometry/footprints and more idealised surface temperature distributions driven only by shadow patterns) gives the best agreement in simulated LST to the realistic landscape. The directional variations are still captured in total (daytime mean absolute error 0.44 K) when using an idealised ORM representation of the same landscape (cuboid buildings, simplified surface temperature) except for roofs which are near-isotropic. Results suggest that geometry assumptions used in current ORM are suited for evaluation of parametric models used to develop and verify operational LST sensor view angle corrections. Future work should consider more realistic materials and scattering processes including low emissivity glass and metals with challenging specular properties.

Evaluation of global climate models for the simulation of precipitation and maximum and minimum temperatures at coarser and finer resolutions based on temporal and spatial assessment metrics in mainland of China

Abstract Many studies have evaluated the performance of multiple global climate models (GCMs) from a temporal or spatial perspective at finer resolution, but no study has evaluated the performance of individual GCMs at different resolutions and before and after bias correction from both temporal and spatial perspectives. The goal of this study is to evaluate the performance of 21 Coupled Model Inter-comparison Project 6 (CMIP6) GCMs at the raw (coarser) and downscaled (finer) resolutions and after bias correction in relation to their skills in the simulation of daily precipitation and maximum and minimum temperatures over China for the period 1961–2014 using state-of-the-art temporal and spatial metrics, Kolmogorov–Smirnov statistic and SPAtial EFficiency. The results indicated some differences in the ranks of GCMs between temporal and spatial metrics at different resolutions. The overall ranking shows that the simulations at the raw resolution of GCMs are more similar to the observations than the simulations after inverse distance weighted interpolation in SPAtial EFficiency. Three variables from bias-corrected GCMs ranked from 1 to 21 show similar good performance in spatial patterns but the poorest trend in empirical Cumulative Distribution Functions (ECDFs) except daily precipitation.

Dynamic and thermodynamic influences on precipitation in Northeast Mexico on orbital to millennial timescales

The timing and mechanisms of past hydroclimate change in northeast Mexico are poorly constrained, limiting our ability to evaluate climate model performance. To address this, we present a multiproxy speleothem record of past hydroclimate variability spanning 62.5 to 5.1 ka from Tamaulipas, Mexico. Here we show a strong influence of Atlantic and Pacific sea surface temperatures on orbital and millennial scale precipitation changes in the region. Multiple proxies show no clear response to insolation forcing, but strong evidence for dry conditions during Heinrich Stadials. While these trends are consistent with other records from across Mesoamerica and the Caribbean, the relative importance of thermodynamic and dynamic controls in driving this response is debated. An isotope-enabled climate model shows that cool Atlantic SSTs and stronger easterlies drive a strong inter-basin sea surface temperature gradient and a southward shift in moisture convergence, causing drying in this region.

Regime-oriented causal model evaluation of Atlantic–Pacific teleconnections in CMIP6

Abstract. The climate system and its spatio-temporal changes are strongly affected by modes of long-term internal variability, like the Pacific decadal variability (PDV) and the Atlantic multidecadal variability (AMV). As they alternate between warm and cold phases, the interplay between PDV and AMV varies over decadal to multidecadal timescales. Here, we use a causal discovery method to derive fingerprints in the Atlantic–Pacific interactions and to investigate their phase-dependent changes. Dependent on the phases of PDV and AMV, different regimes with characteristic causal fingerprints are identified in reanalyses in a first step. In a second step, a regime-oriented causal model evaluation is performed to evaluate the ability of models participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing the observed changing interactions between PDV, AMV and their extra-tropical teleconnections. The causal graphs obtained from reanalyses detect a direct opposite-sign response from AMV to PDV when analyzing the complete 1900–2014 period and during several defined regimes within that period, for example, when AMV is going through its negative (cold) phase. Reanalyses also demonstrate a same-sign response from PDV to AMV during the cold phase of PDV. Historical CMIP6 simulations exhibit varying skill in simulating the observed causal patterns. Generally, large-ensemble (LE) simulations showed better network similarity when PDV and AMV were out of phase compared to other regimes. Also, the two largest ensembles (in terms of number of members) were found to contain realizations with similar causal fingerprints to observations. For most regimes, these same models showed higher network similarity when compared to each other. This work shows how causal discovery on LEs complements the available diagnostics and statistical metrics of climate variability to provide a powerful tool for climate model evaluation.

Incorporation of aerosol into the COSPv2 satellite lidar simulator for climate model evaluation

Abstract. Atmospheric aerosol has substantial impacts on climate, air quality and biogeochemical cycles, and its concentrations are highly variable in space and time. A key variability to evaluate within models that simulate aerosol is the vertical distribution, which influences atmospheric heating profiles and aerosol–cloud interactions, to help constrain aerosol residence time and to better represent the magnitude of simulated impacts. To ensure a consistent comparison between modeled and observed vertical distribution of aerosol, we implemented an aerosol lidar simulator within the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package version 2 (COSPv2). We assessed the attenuated total backscattered (ATB) signal and the backscatter ratios (SRs) at 532 nm in the U.S. Department of Energy's Energy Exascale Earth System Model version 1 (E3SMv1). The simulator performs the computations at the same vertical resolution as the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), making use of aerosol optics from the E3SMv1 model as inputs and assuming that aerosol is uniformly distributed horizontally within each model grid box. The simulator applies a cloud masking and an aerosol detection threshold to obtain the ATB and SR profiles that would be observed above clouds by CALIOP with its aerosol detection capability. Our analysis shows that the aerosol distribution simulated at a seasonal timescale is generally in good agreement with observations. Over the Southern Ocean, however, the model does not produce the SR maximum as observed in the real world. Comparison between clear-sky and all-sky SRs shows little differences, indicating that the cloud screening by potentially incorrect model clouds does not affect the mean aerosol signal averaged over a season. This indicates that the differences between observed and simulated SR values are due not to sampling errors, but to deficiencies in the representation of aerosol in models. Finally, we highlight the need for future applications of lidar observations at multiple wavelengths to provide insights into aerosol properties and distribution and their representation in Earth system models.

Quantifying the physical processes leading to atmospheric hot extremes at a global scale

Heat waves are among the deadliest climate hazards. Yet the relative importance of the physical processes causing their near-surface temperature anomalies (\U0001d447′)—advection of air from climatologically warmer regions, adiabatic warming in subsiding air and diabatic heating—is still a matter of debate. Here we quantify the importance of these processes by evaluating the \U0001d447′ budget along air-parcel backward trajectories. We first show that the extreme near-surface \U0001d447′ during the June 2021 heat wave in western North America was produced primarily by diabatic heating and, to a smaller extent, by adiabatic warming. Systematically decomposing \U0001d447′ during the hottest days of each year (TX1day events) in 1979–2020 globally, we find strong geographical variations with a dominance of advection over mid-latitude oceans, adiabatic warming near mountain ranges and diabatic heating over tropical and subtropical land masses. In many regions, however, TX1day events arise from a combination of these processes. In the global mean, TX1day anomalies form along trajectories over roughly 60 h and 1,000 km, although with large regional variability. This study thus reveals inherently non-local and regionally distinct formation pathways of hot extremes, quantifies the crucial factors determining their magnitude and enables new quantitative ways of climate model evaluation regarding hot extremes.

A range of outcomes: the combined effects of internal variability and anthropogenic forcing on regional climate trends over Europe

Abstract. Disentangling the effects of internal variability and anthropogenic forcing on regional climate trends remains a key challenge with far-reaching implications. Due to its largely unpredictable nature on timescales longer than a decade, internal climate variability limits the accuracy of climate model projections, introduces challenges in attributing past climate changes, and complicates climate model evaluation. Here, we highlight recent advances in climate modeling and physical understanding that have led to novel insights about these key issues. In particular, we synthesize new findings from large-ensemble simulations with Earth system models, observational large ensembles, and dynamical adjustment methodologies, with a focus on European climate.

Evaluation of global teleconnections in CMIP6 climate projections using complex networks

Abstract. In climatological research, the evaluation of climate models is one of the central research subjects. As an expression of large-scale dynamical processes, global teleconnections play a major role in interannual to decadal climate variability. Their realistic representation is an indispensable requirement for the simulation of climate change, both natural and anthropogenic. Therefore, the evaluation of global teleconnections is of utmost importance when assessing the physical plausibility of climate projections. We present an application of the graph-theoretical analysis tool δ-MAPS, which constructs complex networks on the basis of spatio-temporal gridded data sets, here sea surface temperature and geopotential height at 500 hPa. Complex networks complement more traditional methods in the analysis of climate variability, like the classification of circulation regimes or empirical orthogonal functions, assuming a new non-linear perspective. While doing so, a number of technical tools and metrics, borrowed from different fields of data science, are implemented into the δ-MAPS framework in order to overcome specific challenges posed by our target problem. Those are trend empirical orthogonal functions (EOFs), distance correlation and distance multicorrelation, and the structural similarity index. δ-MAPS is a two-stage algorithm. In the first place, it assembles grid cells with highly coherent temporal evolution into so-called domains. In a second step, the teleconnections between the domains are inferred by means of the non-linear distance correlation. We construct 2 unipartite and 1 bipartite network for 22 historical CMIP6 climate projections and 2 century-long coupled reanalyses (CERA-20C and 20CRv3). Potential non-stationarity is taken into account by the use of moving time windows. The networks derived from projection data are compared to those from reanalyses. Our results indicate that no single climate projection outperforms all others in every aspect of the evaluation. But there are indeed models which tend to perform better/worse in many aspects. Differences in model performance are generally low within the geopotential height unipartite networks but higher in sea surface temperature and most pronounced in the bipartite network representing the interaction between ocean and atmosphere.

Evaluation of the regional climate model for the forest area of Yunnan in China

Climate change is becoming increasingly severe. Today, several studies have found that climate change substantially influences the increasing number of forest fires. Regional climate models (RCMs) are currently a vital tool for climate forecasting in researching how to combat forest fires. As China’s forest fire area, Yunnan province has frequent forest fires that generate significant losses, so it is a crucial area for forest fire prevention in China. Therefore, this study uses meteorological observational data from 25 stations in Yunnan over the period 2004–2018 to compares and evaluates the Regional Climate Forecast Model (RegCM) and Weather Research and Forecasting model (WRF) in multiple dimensions. The optimal RCM is then determined for the forest area of Yunnan. The results show that the deviations of RegCM predictions from the spatial mean of the real temperature are less than 3°C, whereas the deviations of WRF are all greater than 3°C. In addition, the RegCM correlation coefficient exceeds 0.8, whereas the WRF correlation coefficient exceeds 0.75. In terms of precipitation, the deviation of RegCM predictions for the whole territory is less than 2 mm, whereas the overall deviation of WRF predictions is great. The correlation coefficient for RegCM and WRF are both less than 0.5, but the RegCM correlation coefficient exceeds that of the WRF. We thus conclude that RegCM is more suitable for predicting the climate of the forest area of Yunnan. This study also provides references for related climate forecasting and research into forest fire dynamics in general.

Assessment of photosynthetic carbon sequestration efficiency of forest vegetation based on photosynthetic mechanism model

The photosynthetic carbon sequestration efficiency of forest vegetation is an important index to evaluate the potential of forest carbon sequestration. Due to the fact that the existing studies mainly use the photosynthetic model of CO2 flux to estimate the carbon flux, and there are great differences in vegetation types and climatic conditions, the model cannot be directly applied to evaluate the photosynthetic carbon sequestration efficiency of forest vegetation. In order to solve this problem, on the basis of summarizing the existing estimation models of the photosynthetic carbon sequestration efficiency of forest vegetation, this paper combined climate model and ecosystem service value evaluation theory to analyze the feasibility of the photosynthetic mechanism model to evaluate the photosynthetic carbon sequestration efficiency of forest vegetation. In order to accurately estimate the photosynthetic carbon sequestration efficiency of forest vegetation under different climatic conditions, a method combining multi-scale and multi-source data was proposed to modify the photosynthetic mechanism model.

Machine-Learned Cloud Classes From Satellite Data for Process-Oriented Climate Model Evaluation

Clouds play a key role in regulating climate change but are difficult to simulate within Earth system models (ESMs). Improving the representation of clouds is one of the key tasks towards more robust climate change projections. This study introduces a new machine-learning based framework relying on satellite observations to improve understanding of the representation of clouds and their relevant processes in climate models. The proposed method is capable of assigning distributions of established cloud types to coarse data. It facilitates a more objective evaluation of clouds in ESMs and improves the consistency of cloud process analysis. The method is built on satellite data from the MODIS instrument labelled by deep neural networks with cloud types defined by the World Meteorological Organization (WMO), using cloud type labels from CloudSat as ground truth. The method is applicable to datasets with information about physical cloud variables comparable to MODIS satellite data and at sufficiently high temporal resolution. We apply the method to alternative satellite data from the Cloud\_cci project (ESA Climate Change Initiative), coarse-grained to typical resolutions of climate models. The resulting cloud type distributions are physically consistent and the horizontal resolutions typical of ESMs are sufficient to apply our method. We recommend outputting crucial variables required by our method for future ESM data evaluation. This will enable the use of labelled satellite data for a more systematic evaluation of clouds in climate models.

A conditional approach for joint estimation of wind speed and direction under future climates

Abstract. This study develops a statistical conditional approach to evaluate climate model performance in wind speed and direction and to project their future changes under the Representative Concentration Pathway (RCP) 8.5 scenario over inland and offshore locations across the continental United States (CONUS). The proposed conditional approach extends the scope of existing studies by a combined characterization of the wind direction distribution and conditional distribution of wind on the direction, hence enabling an assessment of the joint wind speed and direction distribution and their changes. A von Mises mixture distribution is used to model wind directions across models and climate conditions. Wind speed distributions conditioned on wind direction are estimated using two statistical methods, i.e., a Weibull distributional regression model and a quantile regression model, both of which enforce the circular constraint to their resultant estimated distributions. Projected uncertainties associated with different climate models and model internal variability are investigated and compared with the climate change signal to quantify the robustness of the future projections. In particular, this work extends the concept of internal variability in the climate mean to the standard deviation and high quantiles to assess the relative magnitudes to their projected changes. The evaluation results show that the studied climate model captures both historical wind speed and wind direction and their dependencies reasonably well over both inland and offshore locations. Under the RCP8.5 scenario, most of the studied locations show no significant changes in the mean wind speeds in both winter and summer, while the changes in the standard deviation and 95th quantile show some robust changes over certain locations in winter. Specifically, high wind speeds (95th quantile) conditioned on direction in winter are projected to decrease in the northwestern, Colorado, and northern Great Plains locations in our study. In summer, high wind speeds conditioned on direction over the southern Great Plains increase slightly, while high wind speeds conditioned on direction over offshore locations do not change much. The proposed conditional approach enables a combined characterization of the wind speed distributions conditioned on direction and wind direction distributions, which offers a flexible alternative that can provide additional insights for the joint assessment of speed and direction.

Evaluation of Global Climate Models (GCMs) in Representing Climate Phenomena Signatures Over Maritime Continent

The improvements of resolution and physical parameterization in the GCMs from Coupled Model Intercomparison Project Phase 5 (CMIP5) to Phase 6 (CMIP6) do not always guarantee the better representation of climate in several regions. The Maritime Continent (MC) region is influenced by dominant modes of climate phenomena such as the Madden Julian Oscillation (MJO), Indian Ocean Dipole (IOD), and El Niño-Southern Oscillation (ENSO). However, the representation of those phenomena from CMIP5 to CMIP6 models over MC has not been assessed yet. This study evaluates the signature of those phenomena on daily precipitation data of twelve models from each CMIP5 and CMIP6 against ERA-5 (as a reference) in the historical period (1981-2005) over MC. We perform Multiple Linear Regression (MLR) to assess the spatial signatures on daily precipitation anomalies and quantify the spatial similarity between models and reference using Cosine Similarity (CS). Then, we rank the CS values to obtain the best model. Result shows that only half of 12 models in CMIP6 improve from the models in CMIP5. Overall, MIROC5 (CMIP5) is the best model in representing the signature of those phenomena. Therefore, this study recommends the best models for climate change projection and adaptation strategy over the MC.

Internal variability and forcing influence model–satellite differences in the rate of tropical tropospheric warming

Climate-model simulations exhibit approximately two times more tropical tropospheric warming than satellite observations since 1979. The causes of this difference are not fully understood and are poorly quantified. Here, we apply machine learning to relate the patterns of surface-temperature change to the forced and unforced components of tropical tropospheric warming. This approach allows us to disentangle the forced and unforced change in the model-simulated temperature of the midtroposphere (TMT). In applying the climate-model-trained machine-learning framework to observations, we estimate that external forcing has produced a tropical TMT trend of 0.25 ± 0.08 K⋅decade-1 between 1979 and 2014, but internal variability has offset this warming by 0.07 ± 0.07 K⋅decade-1. Using the Community Earth System Model version 2 (CESM2) large ensemble, we also find that a discontinuity in the variability of prescribed biomass-burning aerosol emissions artificially enhances simulated tropical TMT change by 0.04 K⋅decade-1. The magnitude of this aerosol-forcing bias will vary across climate models, but since the latest generation of climate models all use the same emissions dataset, the bias may systematically enhance climate-model trends over the satellite era. Our results indicate that internal variability and forcing uncertainties largely explain differences in satellite-versus-model warming and are important considerations when evaluating climate models.