Physical Climate Models Research Articles

Frontiers in attributing climate extremes and associated impacts

The field of extreme event attribution (EEA) has rapidly developed over the last two decades. Various methods have been developed and implemented, physical modelling capabilities have generally improved, the field of impact attribution has emerged, and assessments serve as a popular communication tool for conveying how climate change is influencing weather and climate events in the lived experience. However, a number of non-trivial challenges still remain that must be addressed by the community to secure further advancement of the field whilst ensuring scientific rigour and the appropriate use of attribution findings by stakeholders and associated applications. As part of a concept series commissioned by the World Climate Research Programme, this article discusses contemporary developments and challenges over six key domains relevant to EEA, and provides recommendations of where focus in the EEA field should be concentrated over the coming decade. These six domains are: (1) observations in the context of EEA; (2) extreme event definitions; (3) statistical methods; (4) physical modelling methods; (5) impact attribution; and (6) communication. Broadly, recommendations call for increased EEA assessments and capacity building, particularly for more vulnerable regions; contemporary guidelines for assessing the suitability of physical climate models; establishing best-practice methodologies for EEA on compound and record-shattering extremes; co-ordinated interdisciplinary engagement to develop scaffolding for impact attribution assessments and their suitability for use in broader applications; and increased and ongoing investment in EEA communication. To address these recommendations requires significant developments in multiple fields that either underpin (e.g., observations and monitoring; climate modelling) or are closely related to (e.g., compound and record-shattering events; climate impacts) EEA, as well as working consistently with experts outside of attribution and climate science more generally. However, if approached with investment, dedication, and coordination, tackling these challenges over the next decade will ensure robust EEA analysis, with tangible benefits to the broader global community.

СОВРЕМЕННЫЕ И БУДУЩИЕ ИЗМЕНЕНИЯ КЛИМАТА ЛЕНИНГРАДСКОЙ ОБЛАСТИ И ИХ ВЛИЯНИЕ НА АГРОКЛИМАТИЧЕСКИЕ ХАРАКТЕРИСТИКИ

Based on the long-term time series of observations at the weather stations in the Leningrad oblast, current changes in monthly mean air temperatures and precipitation, as well as in the agroclimatic characteristics of the growing season are assessed using statistical models of nonstationary means. The spatial distribution of these changes is determined. The most suitable physical and mathematical climate model is selected based on the comparison of observational data with the results of the CMIP historical experiment. Based on the identified systematic model errors, the scenario values of air temperature and precipitation are corrected, and the agroclimatic characteristics until the end of the 21st century for different parts of the region are obtained on their basis. An estimated future yield and recommendations for the output of the most promising agricultural products are given.

Проблемы параметризации радиационного блока в физико-математических моделях климата и возможности их решения

Проблемы параметризации радиационного блока в физико-математических моделях климата и возможности их решения

Проблемы параметризации радиационного блока в физико-математических моделях климата и возможности их решения

Проблемы параметризации радиационного блока в физико-математических моделях климата и возможности их решения

Performance of Two‐Moment Stratiform Microphysics With Prognostic Precipitation in GFDL's CM4.0

AbstractWe describe the model performance of a new global coupled climate model configuration, CM4‐MG2. Beginning with the Geophysical Fluid Dynamics Laboratory's fourth‐generation physical climate model (CM4.0), we incorporate a two‐moment Morrison‐Gettelman bulk stratiform microphysics scheme with prognostic precipitation (MG2), and a mineral dust and temperature‐dependent cloud ice nucleation scheme. We then conduct and analyze a set of fully coupled atmosphere‐ocean‐land‐sea ice simulations, following Coupled Model Intercomparison Project Phase 6 protocols. CM4‐MG2 generally captures CM4.0's baseline simulation characteristics, but with several improvements, including better marine stratocumulus clouds off the west coasts of Africa and North and South America, a reduced bias toward “double” Intertropical Convergence Zones south of the equator, and a stronger Madden‐Julian Oscillation (MJO). Some degraded features are also identified, including excessive Arctic sea ice extent and a stronger‐than‐observed El Nio‐Southern Oscillation. Compared to CM4.0, the climate sensitivity is reduced by about 10% in CM4‐MG2.

The DOE E3SM Model Version 2: Overview of the Physical Model and Initial Model Evaluation

AbstractThis work documents version two of the Department of Energy's Energy Exascale Earth System Model (E3SM). E3SMv2 is a significant evolution from its predecessor E3SMv1, resulting in a model that is nearly twice as fast and with a simulated climate that is improved in many metrics. We describe the physical climate model in its lower horizontal resolution configuration consisting of 110 km atmosphere, 165 km land, 0.5° river routing model, and an ocean and sea ice with mesh spacing varying between 60 km in the mid‐latitudes and 30 km at the equator and poles. The model performance is evaluated with Coupled Model Intercomparison Project Phase 6 Diagnosis, Evaluation, and Characterization of Klima simulations augmented with historical simulations as well as simulations to evaluate impacts of different forcing agents. The simulated climate has many realistic features of the climate system, with notable improvements in clouds and precipitation compared to E3SMv1. E3SMv1 suffered from an excessively high equilibrium climate sensitivity (ECS) of 5.3 K. In E3SMv2, ECS is reduced to 4.0 K which is now within the plausible range based on a recent World Climate Research Program assessment. However, a number of important biases remain including a weak Atlantic Meridional Overturning Circulation, deficiencies in the characteristics and spectral distribution of tropical atmospheric variability, and a significant underestimation of the observed warming in the second half of the historical period. An analysis of single‐forcing simulations indicates that correcting the historical temperature bias would require a substantial reduction in the magnitude of the aerosol‐related forcing.

Interactive influence of ENSO and IOD on contiguous heatwaves in Australia

Australian heatwaves have a significant impact on society. Most previous studies focus on understanding them in terms of frequency, duration, intensity, and timing. However, understanding the spatial characteristics of heatwaves, particularly those occurring in contiguous regions at the same time (here referred to as contiguous heatwaves), is still largely unexplored. Here, we analyse changes in spatial characteristics of contiguous heatwaves in Australia during 1958–2020 using observational data. Our results show that extremely large contiguous heatwaves are covering significantly larger areas and getting significantly longer during the recent period (1989/90–2019/20) compared to the historical period (1958/59–1988/89). We also investigated the association of contiguous heatwaves in Australia with interactions of the El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) using a large multi-member ensemble of a physical climate model. We found that areal magnitude, total area, median duration, and maximum area of large and extremely large contiguous heatwaves in Australia are significantly higher (lower) during the strong El Niño (Es), strong El Niño co-occurring with strong IOD positive (Es-IPs), and with moderate IOD positive (Es-IPm) (co-occurring strong La Niña with the strong IOD negative (Ls-INs)) seasons relative to the neutral seasons (where both ENSO and IOD are in neutral phase). During the Es, Es-IPm, and Es-IPs seasons, the large-scale physical mechanisms are characterised by anticyclonic highs over the southeast and cyclonic lows over the northwest of Australia, favouring the occurrence and intensification of heatwaves in Australia. These results provide insights into the driving mechanisms of contiguous heatwaves in Australia.

Correcting Weather and Climate Models by Machine Learning Nudged Historical Simulations

AbstractDue to limited resolution and inaccurate physical parameterizations, weather and climate models consistently develop biases compared to the observed atmosphere. Using the FV3GFS model at coarse resolution, we propose a method of machine learning corrective tendencies from a hindcast simulation nudged toward observational analysis. We show that a random forest can predict the nudging tendencies from this hindcast simulation with moderate skill using only the model state as input. This random forest is then coupled to FV3GFS, adding corrective tendencies of temperature, specific humidity and horizontal winds at each timestep. The coupled model shows no signs of instability in year‐long simulations and has significant reductions in short‐term forecast error for 500 hPa height, surface pressure and near‐surface temperature. Furthermore, the root mean square error of the annual‐mean precipitation is reduced by about 20%. Biases of other variables remain similar or in some cases, like upper‐atmospheric temperature, increase in the year‐long simulations.

Evaluating the physical and biogeochemical state of the global ocean component of UKESM1 in CMIP6 historical simulations

Abstract. The ocean plays a key role in modulating the climate of the Earth system (ES). At the present time it is also a major sink both for the carbon dioxide (CO2) released by human activities and for the excess heat driven by the resulting atmospheric greenhouse effect. Understanding the ocean's role in these processes is critical for model projections of future change and its potential impacts on human societies. A necessary first step in assessing the credibility of such future projections is an evaluation of their performance against the present state of the ocean. Here we use a range of observational fields to validate the physical and biogeochemical performance of the ocean component of UKESM1, a new Earth system model (ESM) for CMIP6 built upon the HadGEM3-GC3.1 physical climate model. Analysis focuses on the realism of the ocean's physical state and circulation, its key elemental cycles, and its marine productivity. UKESM1 generally performs well across a broad spectrum of properties, but it exhibits a number of notable biases. Physically, these include a global warm bias inherited from model spin-up, excess northern sea ice but insufficient southern sea ice and sluggish interior circulation. Biogeochemical biases found include shallow remineralization of sinking organic matter, excessive iron stress in regions such as the equatorial Pacific, and generally lower surface alkalinity that results in decreased surface and interior dissolved inorganic carbon (DIC) concentrations. The mechanisms driving these biases are explored to identify consequences for the behaviour of UKESM1 under future climate change scenarios and avenues for model improvement. Finally, across key biogeochemical properties, UKESM1 improves in performance relative to its CMIP5 precursor and performs well alongside its fellow members of the CMIP6 ensemble.

The Evaluation of the North Atlantic Climate System in UKESM1 Historical Simulations for CMIP6

AbstractEarth system models enable a broad range of climate interactions that physical climate models are unable to simulate. However, the extent to which adding Earth system components changes or improves the simulation of the physical climate is not well understood. Here we present a broad multivariate evaluation of the North Atlantic climate system in historical simulations of the UK Earth System Model (UKESM1) performed for CMIP6. In particular, we focus on the mean state and the decadal time scale evolution of important variables that span the North Atlantic climate system. In general, UKESM1 performs well and realistically simulates many aspects of the North Atlantic climate system. Like the physical version of the model, we find that changes in external forcing, and particularly aerosol forcing, are an important driver of multidecadal change in UKESM1, especially for Atlantic Multidecadal Variability and the Atlantic Meridional Overturning Circulation. However, many of the shortcomings identified are similar to common biases found in physical climate models, including the physical climate model that underpins UKESM1. For example, the summer jet is too weak and too far poleward; decadal variability in the winter jet is underestimated; intraseasonal stratospheric polar vortex variability is poorly represented; and Arctic sea ice is too thick. Forced shortwave changes may be also too strong in UKESM1, which, given the important role of historical aerosol forcing in shaping the evolution of the North Atlantic in UKESM1, motivates further investigation. Therefore, physical model development, alongside Earth system development, remains crucial in order to improve climate simulations.

CMIP6/PMIP4 simulations of the mid-Holocene and Last Interglacial using HadGEM3: comparison to the pre-industrial era, previous model versions and proxy data

Abstract. Palaeoclimate model simulations are an important tool to improve our understanding of the mechanisms of climate change. These simulations also provide tests of the ability of models to simulate climates very different to today. Here we present the results from two brand-new simulations using the latest version of the UK's physical climate model, HadGEM3-GC3.1; they are the mid-Holocene (∼6 ka) and Last Interglacial (∼127 ka) simulations, both conducted under the auspices of CMIP6/PMIP4. This is the first time this version of the UK model has been used to conduct palaeoclimate simulations. These periods are of particular interest to PMIP4 because they represent the two most recent warm periods in Earth history, where atmospheric concentration of greenhouse gases and continental configuration are similar to the pre-industrial period but where there were significant changes to the Earth's orbital configuration, resulting in a very different seasonal cycle of radiative forcing. Results for these simulations are assessed firstly against the same model's pre-industrial control simulation (a simulation comparison, to describe and understand the differences between the pre-industrial – PI – and the two palaeo simulations) and secondly against previous versions of the same model relative to newly available proxy data (a model–data comparison, to compare all available simulations from the same model with proxy data to assess any improvements due to model advances). The introduction of this newly available proxy data adds further novelty to this study. Globally, for metrics such as 1.5 m temperature and surface rainfall, whilst both the recent palaeoclimate simulations are mostly capturing the expected sign and, in some places, magnitude of change relative to the pre-industrial, this is geographically and seasonally dependent. Compared to newly available proxy data (including sea surface temperature – SST – and rainfall) and also incorporating data from previous versions of the model shows that the relative accuracy of the simulations appears to vary according to metric, proxy reconstruction used for comparison and geographical location. In some instances, such as mean rainfall in the mid-Holocene, there is a clear and linear improvement, relative to proxy data, from the oldest to the newest generation of the model. When zooming into northern Africa, a region known to be problematic for models in terms of rainfall enhancement, the behaviour of the West African monsoon in both recent palaeoclimate simulations is consistent with current understanding, suggesting a wetter monsoon during the mid-Holocene and (more so) the Last Interglacial, relative to the pre-industrial era. However, regarding the well-documented “Saharan greening” during the mid-Holocene, results here suggest that the most recent version of the UK's physical model is still unable to reproduce the increases suggested by proxy data, consistent with all other previous models to date.

Development of a postprocessing system of daily rainfall forecasts for seasonal crop prediction in Australia

There is a need to postprocess seasonal rainfall forecasts from physical climate models to reduce bias, improve skill and restore daily variability for use as input for crop simulation at the farm scale. We develop an extended copula postprocessing (ECPP) method to deal with daily rainfall with numerous zero occurrences. By treating rainfall as a left-censored variable, we derive likelihood estimation and adjust simulation procedure with consideration of zero rainfall occurrences. In a case study for 50 representative agricultural stations in Australia, we test our method to postprocess daily rainfall forecasts with up to 186-day lead time. We demonstrate that the ECPP improves the overall forecast skill from raw rainfall forecasts and outperforms quantile mapping (QM) by checking various verification measures. Though the forecasts for daily amounts are hardly skilful except for the first few days, the forecasts for accumulated totals can be skilful from error averaging and propagating positive skill from short lead times. We also demonstrate that the ECPP can simulate rainfall forecasts with more realistic dry day distribution and daily rainfall intensity than QM. Further research directions including several opportunities to improve ECPP are discussed.

The development and prudent application of climate-based forecasts of seasonal malaria in the Limpopo province in South Africa

Seasonal Climate Forecasting (SCF) in South Africa has a history spanning several decades. During this period a number of SCF systems have been developed for the prediction of seasonal-to-interannual variability of rainfall and surface temperatures. Areas of highest predictability, albeit relatively modest, have also been identified. The north-eastern parts of South Africa that includes the Limpopo province has been demonstrated to be one of the areas of highest SCF skill in the country. Statistical post-processing techniques applied to global climate model output were part of this forecast system development, and were subsequently successfully used in the construction of forecasts systems for applications in sectors which are associated with ENSO-driven climate variability, such as dry-land crop yields and river flows. Here we follow a similar post-model processing approach to test SCF systems for application to the incidence of seasonal malaria in Limpopo. The malaria forecast system introduced here makes use of the seasonal rainfall output fields of one of the North American Multi-Model Ensemble (NMME) climate models, which is then linked statistically through multiple linear regression to observed malaria incidence. The verification results as calculated over a 20-year hindcast period show that the season of highest malaria incidence forecast skill is during the austral mid-summer time of December to February. Moreover, the hindcasts based on the NMME model outscore those of statistical forecast models that separately use Indian and Pacific Ocean sea-surface temperatures as predictors, thus justifying the use of physical global climate models for this kind of application. Additional results indicate that model skill levels may include quasi-decadal variability, that the periods over which forecast verification is performed strongly influences forecast skill, and that poorly predicted malaria seasons may have serious financial implications on public health operations.

Built form, urban climate and building energy modelling: case-studies in Rome and Antofagasta

The energy performance of urban buildings is affected by multiple climate phenomena such as heat island intensity, wind flow, solar obstructions and infrared radiation exchange in urban canyons, but a modelling procedure to account for all of them in building performance simulation is still missing. This paper contributes to fill this gap by describing a chain strategy to model urban boundary conditions suitable for annual simulations using dynamic thermal simulation tools. The methodology brings together existing physical and empirical climate models and it is applied to 10 case studies in Rome (Italy) and Antofagasta (Chile). The results show that urban climate varies significantly across a city depending on the density of urban texture and its impact on the annual energy demand depends on the region's climate. The urban shadows are crucial in cooling-dominated climates (Antofagasta) while the urban heat island intensity is more important in temperate climates (Rome).Abbreviations: ACH: Air change per hour; BPS: Building Performance Simulation; BS: British Standard; CNV: Controlled natural ventilation; H/W: height-to-width ratio of urban canyons; L/W: length-to-width ratio of urban canyons; UHI: Urban Heat Island; UWG: Urban Weather Generator model

Forcings, Feedbacks, and Climate Sensitivity in HadGEM3‐GC3.1 and UKESM1

AbstractClimate forcing, sensitivity, and feedback metrics are evaluated in both the United Kingdom's physical climate model HadGEM3‐GC3.1 at low (‐LL) and medium (‐MM) resolution and the United Kingdom's Earth System Model UKESM1. The effective climate sensitivity (EffCS) to a doubling of CO2 is 5.5 K for HadGEM3.1‐GC3.1‐LL and 5.4 K for UKESM1. The transient climate response is 2.5 and 2.8 K, respectively. While the EffCS is larger than that seen in the previous generation of models, none of the model's forcing or feedback processes are found to be atypical of models, though the cloud feedback is at the high end. The relatively large EffCS results from an unusual combination of a typical CO2 forcing with a relatively small feedback parameter. Compared to the previous U.K. climate model, HadGEM3‐GC2.0, the EffCS has increased from 3.2 to 5.5 K due to an increase in CO2 forcing, surface albedo feedback, and midlatitude cloud feedback. All changes are well understood and due to physical improvements in the model. At higher atmospheric and ocean resolution (HadGEM3‐GC3.1‐MM), there is a compensation between increased marine stratocumulus cloud feedback and reduced Antarctic sea‐ice feedback. In UKESM1, a CO2 fertilization effect induces a land surface vegetation change and albedo radiative effect. Historical aerosol forcing in HadGEM3‐GC3.1‐LL is −1.1 W m−2. In HadGEM3‐GC3.1‐LL historical simulations, cloud feedback is found to be less positive than in abrupt‐4xCO2, in agreement with atmosphere‐only experiments forced with observed historical sea surface temperature and sea‐ice variations. However, variability in the coupled model's historical sea‐ice trends hampers accurate diagnosis of the model's total historical feedback.

Structure and Performance of GFDL's CM4.0 Climate Model

AbstractWe describe the Geophysical Fluid Dynamics Laboratory's CM4.0 physical climate model, with emphasis on those aspects that may be of particular importance to users of this model and its simulations. The model is built with the AM4.0/LM4.0 atmosphere/land model and OM4.0 ocean model. Topics include the rationale for key choices made in the model formulation, the stability as well as drift of the preindustrial control simulation, and comparison of key aspects of the historical simulations with observations from recent decades. Notable achievements include the relatively small biases in seasonal spatial patterns of top‐of‐atmosphere fluxes, surface temperature, and precipitation; reduced double Intertropical Convergence Zone bias; dramatically improved representation of ocean boundary currents; a high‐quality simulation of climatological Arctic sea ice extent and its recent decline; and excellent simulation of the El Niño‐Southern Oscillation spectrum and structure. Areas of concern include inadequate deep convection in the Nordic Seas; an inaccurate Antarctic sea ice simulation; precipitation and wind composites still affected by the equatorial cold tongue bias; muted variability in the Atlantic Meridional Overturning Circulation; strong 100 year quasiperiodicity in Southern Ocean ventilation; and a lack of historical warming before 1990 and too rapid warming thereafter due to high climate sensitivity and strong aerosol forcing, in contrast to the observational record. Overall, CM4.0 scores very well in its fidelity against observations compared to the Coupled Model Intercomparison Project Phase 5 generation in terms of both mean state and modes of variability and should prove a valuable new addition for analysis across a broad array of applications.

The Impact of Prescribed Ozone in Climate Projections Run With HadGEM3‐GC3.1

AbstractThe Coupled Model Intercomparison Project 6 protocol suggests prescribing preindustrial ozone concentrations in abrupt‐4xCO2 simulations. This leads to a mismatch between the thermal tropopause, which rises due to climate change, and the ozone tropopause, which remains fixed. The result is unphysically high ozone concentrations in the upper troposphere, leading to a warm bias in cold point temperature and increased stratospheric water vapor. In the U.K. physical climate model HadGEM3‐GC3.1 this increases the surface climate sensitivity. In the future, other climate models without interactive ozone schemes may face similar problems. We describe a method to interactively redistribute ozone in climate simulations, which removes the inconsistency between the thermal and ozone tropopause heights while retaining the prescribed ozone distribution as closely as possible. This removes unphysical consequences of the tropopause mismatch, while still allowing a fair comparison against other Coupled Model Intercomparison Project 6 model simulations. After each model year, the monthly mean, zonal mean, thermal tropopause is formed based on the previous two model years. The ozone tropopause is defined at 1 km below the thermal tropopause by setting ozone concentrations there to 80 ppbv, and smoothing appropriately. The mass of ozone removed from the troposphere is added to the stratosphere thus conserving the total mass of ozone. This redistribution is then applied proportionally to the 3‐D monthly mean ozone concentrations. The climate model is run for the following year, using this redistributed ozone, and then the whole process is repeated. Results are presented from preindustrial and abrupt‐4xCO2 simulations, but this method can be used for any climate simulation.

ESD Reviews: Climate feedbacks in the Earth system and prospects for their evaluation

Abstract. Earth system models (ESMs) are key tools for providing climate projections under different scenarios of human-induced forcing. ESMs include a large number of additional processes and feedbacks such as biogeochemical cycles that traditional physical climate models do not consider. Yet, some processes such as cloud dynamics and ecosystem functional response still have fairly high uncertainties. In this article, we present an overview of climate feedbacks for Earth system components currently included in state-of-the-art ESMs and discuss the challenges to evaluate and quantify them. Uncertainties in feedback quantification arise from the interdependencies of biogeochemical matter fluxes and physical properties, the spatial and temporal heterogeneity of processes, and the lack of long-term continuous observational data to constrain them. We present an outlook for promising approaches that can help to quantify and to constrain the large number of feedbacks in ESMs in the future. The target group for this article includes generalists with a background in natural sciences and an interest in climate change as well as experts working in interdisciplinary climate research (researchers, lecturers, and students). This study updates and significantly expands upon the last comprehensive overview of climate feedbacks in ESMs, which was produced 15 years ago (NRC, 2003).

Evaluation and improvement of tail behaviour in the cumulative distribution function transform downscaling method

The cumulative distribution function transform (CDFt) downscaling method has been used widely to provide local‐scale information and bias correction to output from physical climate models. The CDFt approach is one from the category of statistical downscaling methods that operates via transformations between statistical distributions. Although numerous studies have demonstrated that such methods provide value overall, much less effort has focused on their performance with regard to values in the tails of distributions. We evaluate the performance of CDFt‐generated tail values based on four distinct approaches, two native to CDFt and two of our own creation, in the context of a “Perfect Model” setting in which global climate model output is used as a proxy for both observational and model data. We find that the native CDFt approaches can have sub‐optimal performance in the tails, particularly with regard to the maximum value. However, our alternative approaches provide substantial improvement.

Earth’s insolation variation and its incorporation into physical and mathematical climate models

We review research on long-term variations in Earth’s insolation due to celestial mechanics processes. Based on an analytical survey of Earth’s insolation calculations, general problems encountered in the physical and mathematical modeling of climate are outlined.