Climate Model Evaluation Research Articles

Testing the Tests: What Are the Impacts of Incorrect Assumptions When Applying Confidence Intervals or Hypothesis Tests to Compare Competing Forecasts?

Which of two competing continuous forecasts is better? This question is often asked in forecast verification, as well as climate model evaluation. Traditional statistical tests seem to be well suited to the task of providing an answer. However, most such tests do not account for some of the special underlying circumstances that are prevalent in this domain. For example, model output is seldom independent in time, and the models being compared are geared to predicting the same state of the atmosphere, and thus they could be contemporaneously correlated with each other. These types of violations of the assumptions of independence required for most statistical tests can greatly impact the accuracy and power of these tests. Here, this effect is examined on simulated series for many common testing procedures, including two-sample and paired t and normal approximation z tests, the z test with a first-order variance inflation factor applied, and the newer Hering–Genton (HG) test, as well as several bootstrap methods. While it is known how most of these tests will behave in the face of temporal dependence, it is less clear how contemporaneous correlation will affect them. Moreover, it is worthwhile knowing just how badly the tests can fail so that if they are applied, reasonable conclusions can be drawn. It is found that the HG test is the most robust to both temporal dependence and contemporaneous correlation, as well as the specific type and strength of temporal dependence. Bootstrap procedures that account for temporal dependence stand up well to contemporaneous correlation and temporal dependence, but require large sample sizes to be accurate.

Trends in temperature extremes and their association with circulation patterns in China during 1961–2015

Understanding the dynamic changes of climatic extremes is important to predict the occurrences of extreme climate events and to take measures to reduce their associated impacts. Based on daily maximum and minimum temperature data collected from 1899 meteorological stations in China and 8 metrics of large-scale circulation patterns from 1961 to 2015, the spatiotemporal trends in temperature extremes and their associations with circulation patterns were analyzed. The linear trend analysis and Mann-Kendall method were applied to examine the trends, and Pearson correlation analysis was used to identify the relationship between temperature indices and circulations. Results indicated that the increases in tropical nights (TR20), summer days (SU25), warm days (TX90p) and warm nights (TN90p), concurrent with the decreases in frost days (FD0), ice days (ID0), cool days (TX10p) and cool nights (TN10p) were highly pronounced in China as a whole. Spatially, SU25, TN90p and TX90p had increased at rates of 1.5–5.0 days, 0–4.0% and 0–3.0% per decade, while FD0, TN10p and TX10p had decreased at rates of 1.5–6.0 days, 0.5–4.5% and 0–2.1% per decade respectively in almost all of China. The increase of TR20 at rates of 0–6.0 days per decade occurred in most of China except for Qinghai-Tibet Plateau and the northern part of northeastern China, and the decreases of ID0 at rates of 0–6.0 days per decade were appeared mainly in northeastern China, northern China, the middle and lower reaches of the Yangtze river and Qinghai-Tibet Plateau. Changes in temperature extremes were associated with some oceanic and atmospheric circulations, of which Atlantic multidecadal oscillation (AMO) and dipole mode index (DMI) had the highest correlation with temperature indices in China. The correlations between temperature indices and AMO were significant in most of China and those between temperature indices and DIM and East Atlantic/Western Russia (EA/WR) were also significant in some parts of China. The variations of all cold extremes were also negatively related to Arctic oscillation (AO) in northeastern China, northern Xinjiang, the northern parts of northern China and eastern China, and those of TN90p and TX90p were positively related to AO in northeastern China. These findings will provide useful information in forecasting extreme climate events and evaluating climate models.

Interannual rainfall variability over China in the MetUM GA6 and GC2 configurations

Abstract. Six climate simulations of the Met Office Unified Model Global Atmosphere 6.0 and Global Coupled 2.0 configurations are evaluated against observations and reanalysis data for their ability to simulate the mean state and year-to-year variability of precipitation over China. To analyse the sensitivity to air–sea coupling and horizontal resolution, atmosphere-only and coupled integrations at atmospheric horizontal resolutions of N96, N216 and N512 (corresponding to ∼ 200, 90 and 40 km in the zonal direction at the equator, respectively) are analysed. The mean and interannual variance of seasonal precipitation are too high in all simulations over China but improve with finer resolution and coupling. Empirical orthogonal teleconnection (EOT) analysis is applied to simulated and observed precipitation to identify spatial patterns of temporally coherent interannual variability in seasonal precipitation. To connect these patterns to large-scale atmospheric and coupled air–sea processes, atmospheric and oceanic fields are regressed onto the corresponding seasonal mean time series. All simulations reproduce the observed leading pattern of interannual rainfall variability in winter, spring and autumn; the leading pattern in summer is present in all but one simulation. However, only in two simulations are the four leading patterns associated with the observed physical mechanisms. Coupled simulations capture more observed patterns of variability and associate more of them with the correct physical mechanism, compared to atmosphere-only simulations at the same resolution. However, finer resolution does not improve the fidelity of these patterns or their associated mechanisms. This shows that evaluating climate models by only geographical distribution of mean precipitation and its interannual variance is insufficient. The EOT analysis adds knowledge about coherent variability and associated mechanisms.

Scale‐Aware and Definition‐Aware Evaluation of Modeled Near‐Surface Precipitation Frequency Using CloudSat Observations

AbstractCloudSat's 94‐GHz Cloud Profiling Radar provides unique near‐global observations of precipitation frequency and intensity. Here CloudSat‐based diagnostics for near‐surface precipitation frequency are implemented in publicly available software that is widely used for climate model evaluation. The new diagnostics are “definition aware” and “scale aware.” As a result, the diagnostics enable robust assessment of modeled near‐surface precipitation frequency at a range of intensity classes. The new diagnostics are used to evaluate precipitation frequency in a state‐of‐the‐art climate model, the Community Earth System Model version 1 (CESM1). CESM1 rains and snows too frequently, a bias that is especially pronounced for light rain. Conversely, while rare in both observations and CESM1, the heaviest rainfall events occur too infrequently in CESM1. Though the spatial distribution of snowfall events matches observations well, CESM1 also exhibits excessive snow frequency biases. Despite these biases, projected CESM1 changes in reflectivity‐based diagnostics provide interesting insights into what a future 94‐GHz radar could detect in a warmer world. With 3 °C of global warming, a future CloudSat‐class mission would detect substantial conversion of snow to rain at midlatitudes, a narrowing of the Tropical Pacific rain belt, increased light rain in subtropics, and increased snow frequency in polar regions. The future CESM1 simulations also provide evidence that present‐day spatial and magnitude biases imprint themselves on precipitation frequency changes. In summary, new precipitation frequency diagnostics for a range of precipitation intensities robustly expose climate model biases and inform expectations for observable future precipitation changes in a warming world.

Evaluation of CORDEX regional climate models in simulating temperature and precipitation over the Tibetan Plateau

Using a regional climate model (RCM) is generally regarded as a promising approach in researching the climate of the Tibetan Plateau, due to the advantages provided by the high resolutions of these...

Using the Atmospheric Radiation Measurement (ARM) Datasets to Evaluate Climate Models in Simulating Diurnal and Seasonal Variations of Tropical Clouds

AbstractLong-term Atmospheric Radiation Measurement (ARM) datasets collected at the three tropical western Pacific (TWP) sites are used to evaluate the ability of the Community Atmosphere Model (CAM5) to simulate the various types of clouds, their seasonal and diurnal variations, and their impact on surface radiation. A number of CAM5 simulations are conducted at various horizontal grid spacing (around 2°, 1°, 0.5°, and 0.25°) with meteorological constraints from analysis or reanalysis. Model biases in the seasonal cycle of cloudiness are found to be weakly dependent on model resolution. Positive biases (up to 20%) in the annual mean total cloud fraction appear mostly in stratiform ice clouds. Higher-resolution simulations do reduce the positive bias in ice clouds, but they inadvertently increase the negative biases in convective clouds and low-level liquid clouds, leading to a positive bias in annual mean shortwave fluxes at the sites, as high as 65 W m−2 in the 0.25° simulation. Such resolution-dependent biases in clouds can adversely lead to biases in ambient thermodynamic properties and, in turn, produce feedback onto clouds. Both the model and observations show distinct diurnal cycles in total, stratiform, and convective cloud fractions; however, they are out of phase by 12 h and the biases vary by site. The results suggest that biases in deep convection affect the vertical distribution and diurnal cycle of stratiform clouds through the transport of vapor and/or the detrainment of liquid and ice. The approach used here can be easily adapted for the evaluation of new parameterizations being developed for CAM5 or other global or regional models.

Quality assessment of the Ozone_cci Climate Research Data Package (release 2017) – Part 1: Ground-based validation of total ozone column data products

Abstract. The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) is a level-3 data record, which combines individual sensor products into one single cohesive record covering the 22-year period from 1995 to 2016, generated in the frame of the European Space Agency's Climate Change Initiative Phase II. It is based on level-2 total ozone data produced by the GODFIT (GOME-type Direct FITting) v4 algorithm as applied to the GOME/ERS-2, OMI/Aura, SCIAMACHY/Envisat and GOME-2/Metop-A and Metop-B observations. In this paper we examine whether GTO-ECV meets the specific requirements set by the international climate–chemistry modelling community for decadal stability long-term and short-term accuracy. In the following, we present the validation of the 2017 release of the Climate Research Data Package Total Ozone Column (CRDP TOC) at both level 2 and level 3. The inter-sensor consistency of the individual level-2 data sets has mean differences generally within 0.5 % at moderate latitudes (±50°), whereas the level-3 data sets show mean differences with respect to the OMI reference data record that span between −0.2 ± 0.9 % (for GOME-2B) and 1.0 ± 1.4 % (for SCIAMACHY). Very similar findings are reported for the level-2 validation against independent ground-based TOC observations reported by Brewer, Dobson and SAOZ instruments: the mean bias between GODFIT v4 satellite TOC and the ground instrument is well within 1.0 ± 1.0 % for all sensors, the drift per decade spans between −0.5 % and 1.0 ± 1.0 % depending on the sensor, and the peak-to-peak seasonality of the differences ranges from ∼ 1 % for GOME and OMI to ∼ 2 % for SCIAMACHY. For the level-3 validation, our first goal was to show that the level-3 CRDP produces findings consistent with the level-2 individual sensor comparisons. We show a very good agreement with 0.5 to 2 % peak-to-peak amplitude for the monthly mean difference time series and a negligible drift per decade of the differences in the Northern Hemisphere of −0.11 ± 0.10 % decade−1 for Dobson and +0.22 ± 0.08 % decade−1 for Brewer collocations. The exceptional quality of the level-3 GTO-ECV v3 TOC record temporal stability satisfies well the requirements for the total ozone measurement decadal stability of 1–3 % and the short-term and long-term accuracy requirements of 2 and 3 %, respectively, showing a remarkable inter-sensor consistency, both in the level-2 GODFIT v4 and in the level-3 GTO-ECV v3 datasets, and thus can be used for longer-term analysis of the ozone layer, such as decadal trend studies, chemistry–climate model evaluation and data assimilation applications.

Atmospheric QBO and ENSO indices with high vertical resolution from GNSS radio occultation temperature measurements

Abstract. We provide atmospheric temperature variability indices for the tropical troposphere and stratosphere based on global navigation satellite system (GNSS) radio occultation (RO) temperature measurements. By exploiting the high vertical resolution and the uniform distribution of the GNSS RO temperature soundings we introduce two approaches, both based on an empirical orthogonal function (EOF) analysis. The first method utilizes the whole vertical and horizontal RO temperature field from 30∘ S to 30∘ N and from 2 to 35 km altitude. The resulting indices, the leading principal components, resemble the well-known patterns of the Quasi-Biennial Oscillation (QBO) and the El Niño–Southern Oscillation (ENSO) in the tropics. They provide some information on the vertical structure; however, they are not vertically resolved. The second method applies the EOF analysis on each altitude level separately and the resulting indices contain information on the horizontal variability at each densely available altitude level. They capture more variability than the indices from the first method and present a mixture of all variability modes contributing at the respective altitude level, including the QBO and ENSO. Compared to commonly used variability indices from QBO winds or ENSO sea surface temperature, these new indices cover the vertical details of the atmospheric variability. Using them as proxies for temperature variability is also of advantage because there is no further need to account for response time lags. Atmospheric variability indices as novel products from RO are expected to be of great benefit for studies on atmospheric dynamics and variability, for climate trend analysis, as well as for climate model evaluation.

Testing the Hydrological Coherence of High‐Resolution Gridded Precipitation and Temperature Data Sets

AbstractAssessing the accuracy of gridded climate data sets is highly relevant to climate change impact studies, since evaluation, bias correction, and statistical downscaling of climate models commonly use these products as reference. Among all impact studies those addressing hydrological fluxes are the most affected by errors and biases plaguing these data. This paper introduces a framework, coined Hydrological Coherence Test (HyCoT), for assessing the hydrological coherence of gridded data sets with hydrological observations. HyCoT provides a framework for excluding meteorological forcing data sets not complying with observations, as function of the particular goal at hand. The proposed methodology allows falsifying the hypothesis that a given data set is coherent with hydrological observations on the basis of the performance of hydrological modeling measured by a metric selected by the modeler. HyCoT is demonstrated in the Adige catchment (southeastern Alps, Italy) for streamflow analysis, using a distributed hydrological model. The comparison covers the period 1989–2008 and includes five gridded daily meteorological data sets: E‐OBS, MSWEP, MESAN, APGD, and ADIGE. The analysis highlights that APGD and ADIGE, the data sets with highest effective resolution, display similar spatiotemporal precipitation patterns and produce the largest hydrological efficiency indices. Lower performances are observed for E‐OBS, MESAN, and MSWEP, especially in small catchments. HyCoT reveals deficiencies in the representation of spatiotemporal patterns of gridded climate data sets, which cannot be corrected by simply rescaling the meteorological forcing fields, as often done in bias correction of climate model outputs. We recommend this framework to assess the hydrological coherence of gridded data sets to be used in large‐scale hydroclimatic studies.

Numerical simulation of surface solar radiation over Southern Africa. Part 1: Evaluation of regional and global climate models

In the second part of this study, possible impacts of climate change on Surface Solar Radiation (SSR) in Southern Africa (SA) are evaluated. We use outputs from 20 regional climate simulations from five Regional Climate Models (RCM) that participate in the Coordinated Regional Downscaling Experiment program over the African domain (CORDEX-Africa) along with their 10 driving Global Climate Models (GCM) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). Multi-model mean projections of SSR trends are consistent between the GCMs and their nested RCMs. However, this consistency is not found for each GCM/RCM setup. Over the centre of SA, GCMs and RCMs project a statistically significant increase in SSR by 2099 of about + 1 W/m2 per decade in RCP4.5 (+ 1.5 W/m2 per decade in RCP8.5) during the DJF season in their multi-model means. Over Eastern Equatorial Africa (EA-E) a statistically significant decrease in SSR of about − 1.5 W/m2 per decade in RCP4.5 (− 2 W/m2 per decade in RCP8.5) is found in the ensemble means in DJF, whereas in JJA SSR is predicted to increase by about + 0.5 W/m2 per decade under RCP4.5 (+ 1 W/m2 per decade in RCP8.5). SSR projections are fairly similar between RCP8.5 and RCP4.5 before 2050 and then the differences between those two scenarios increase up to about 1 W/m2 per decade with larger changes in RCP8.5 than in RCP4.5 scenario. These SSR evolutions are generally consistent with projected changes in Cloud Cover Fraction over SA and may also related to the changes in atmosphere water vapor content. SSR change signals emerge earlier out of internal variability estimated from reanalyses (European Centre for Medium-Range Weather Forecasts Reanalysis ERA-Interim, ERAIN) in DJF in RCMs than in GCMs, which suggests a higher sensitivity of RCMs to the forcing RCP scenarios than their driving GCMs in simulating SSR changes. Uncertainty in SSR change projections over SA is dominated by the internal climate variability before 2050, and after that model and scenario uncertainties become as important as internal variability until the end of the 21st century.

Evaluation of Precipitation From EURO‐CORDEX Regional Climate Simulations in a Small‐Scale Mediterranean Site

AbstractThe evaluation of regional climate models' (RCMs') ability to reproduce the present‐day climate is critical to support their utility in impact studies under future climatic scenarios. This study evaluates the skill of an ensemble of state‐of‐the‐art regional climate simulations from the European Coordinated Regional Downscaling Experiment initiative in reproducing the precipitation (P) properties in Sardinia, a Mediterranean island of ~24,000 km2. The ensemble includes simulations at 0.44° and 0.11° grid spacing of the “Historical” experiment from 1950 to 2005. Precipitation records from a high‐density network of gauges are used as reference data set. The interannual and seasonal climatology of P, presence of trend, and orographic effect are assessed at the original RCM grid spacings and different spatial scales of aggregation. Most models capture the observed positive relation between annual P and elevation, with better performance at 0.11° spacing. The simulated spatial patterns of the climatological annual and seasonal means are well correlated with the observation at both spacings, but their spatial variability is overestimated. Positive and negative bias of up to ±60% are found in the simulation of annual mean and interannual variability. While the majority of the models reproduce the phase of the seasonal cycle, they underestimate (overestimate) winter (summer) P. The RCMs exhibit different deficiencies in capturing the negative annual and seasonal observed trends. In general, models' skill degrades when analyses are conducted at smaller aggregation scales. Results of this study reveal insight on RCM performances in small‐scale regions, which are often targeted by impact studies and have so far received less attention.

Evaluating Climate Models with an African Lens

Abstract Climate models are becoming evermore complex and increasingly relied upon to inform climate change adaptation. Yet progress in model development is lagging behind in many of the regions that need the information most, including in Africa. Targeted model development for Africa is crucial and so too is targeted model evaluation. Assessment of model performance in specific regions often follows a “validation” approach, focusing on mean biases, but if models are to be improved, it is important to understand how they simulate regional climate dynamics: to move from validation to process-based evaluation. This evaluation may be different for every region and requires local weather and climate expertise: a “one size fits all” approach could overlook important, region-specific phenomena. So which are the important processes in African regions? And how might they be evaluated? This paper addresses these questions, drawing on the expertise of a team of scientists from Central, East, southern, and West Africa. For each region, the current understanding of climate models is reviewed, and an example of targeted evaluation is provided, including analysis of moist circulations, teleconnections, and modes of variability. A pan-African perspective is also considered, to examine processes operating between regions. The analysis is based on the Met Office Unified Model, but it uses diagnostics that might be applied to other models. These examples are intended to prompt further discussion among climate modelers and African scientists about how to best evaluate models with an African lens, and promote the development of a model evaluation hub for Africa, to fast track understanding of model behavior for this important continent.

Simulation of the Central Indian Ocean Mode in CESM: Implications for the Indian Summer Monsoon System

AbstractThe simulation of the Indian summer monsoon and its pronounced intraseasonal component in a modern climate model remains a significant challenge. Recently, using observations and reanalysis products, the central Indian Ocean (CIO) mode was found to be a natural mode in the ocean‐atmosphere coupled system and also shown to have a close mechanistic connection with the monsoon intraseasonal oscillation (MISO). In this study, the simulation of the actual CIO mode in historical Community Earth System Model (CESM) outputs is assessed by comparing with observations and reanalysis products. The simulation of the Madden‐Julian Oscillation, a major component of tropical intraseasonal variabilities (ISVs), is satisfactory. However, the CIO mode is not well captured in any of the CESM simulations considered here. The force and response relationship between the atmosphere and the ocean associated with the CIO mode in CESM is opposite to that in nature. The simulated meridional gradient of large‐scale zonal winds is too weak, which precludes the necessary energy conversion from the mean state to the ISVs and cuts off the energy source to MISO in CESM. The inability of CESM to reproduce the CIO mode seen clearly in nature highlights the CIO mode as a new dynamical framework for diagnosing the deficiencies in Indian summer monsoon simulation in climate models. The CIO mode is a coupled metric for evaluating climate models and may be a better indicator of a model's skill to accurately capture the tropical multiscale interactions over subseasonal to interannual timescales.

Tropospheric Ozone Assessment Report: Present-day distribution and trends of tropospheric ozone relevant to climate and global atmospheric chemistry model evaluation

The Tropospheric Ozone Assessment Report (TOAR) is an activity of the International Global Atmospheric Chemistry Project. This paper is a component of the report, focusing on the present-day distribution and trends of tropospheric ozone relevant to climate and global atmospheric chemistry model evaluation. Utilizing the TOAR surface ozone database, several figures present the global distribution and trends of daytime average ozone at 2702 non-urban monitoring sites, highlighting the regions and seasons of the world with the greatest ozone levels. Similarly, ozonesonde and commercial aircraft observations reveal ozone’s distribution throughout the depth of the free troposphere. Long-term surface observations are limited in their global spatial coverage, but data from remote locations indicate that ozone in the 21st century is greater than during the 1970s and 1980s. While some remote sites and many sites in the heavily polluted regions of East Asia show ozone increases since 2000, many others show decreases and there is no clear global pattern for surface ozone changes since 2000. Two new satellite products provide detailed views of ozone in the lower troposphere across East Asia and Europe, revealing the full spatial extent of the spring and summer ozone enhancements across eastern China that cannot be assessed from limited surface observations. Sufficient data are now available (ozonesondes, satellite, aircraft) across the tropics from South America eastwards to the western Pacific Ocean, to indicate a likely tropospheric column ozone increase since the 1990s. The 2014–2016 mean tropospheric ozone burden (TOB) between 60°N–60°S from five satellite products is 300 Tg ± 4%. While this agreement is excellent, the products differ in their quantification of TOB trends and further work is required to reconcile the differences. Satellites can now estimate ozone’s global long-wave radiative effect, but evaluation is difficult due to limited in situ observations where the radiative effect is greatest.

Large-scale heavy precipitation over central Europe and the role of atmospheric cyclone track types.

ABSTRACTPrecipitation patterns over Europe are largely controlled by atmospheric cyclones embedded in the general circulation of the mid‐latitudes. This study evaluates the climatologic features of precipitation for selected regions in central Europe with respect to cyclone track types for 1959–2015, focusing on large‐scale heavy precipitation.The analysis suggests that each of the cyclone track types is connected to a specific pattern of the upper level atmospheric flow, usually characterized by a major trough located over Europe. A dominant upper level cut‐off low (COL) is found over Europe for strong continental (CON) and van Bebber's type (Vb) cyclones which move from the east and southeast into central Europe. Strong Vb cyclones revealed the longest residence times, mainly due to circular propagation paths.The central European cyclone precipitation climate can largely be explained by seasonal track‐type frequency and cyclone intensity; however, additional factors are needed to explain a secondary precipitation maximum in early autumn. The occurrence of large precipitation totals for track events is strongly related to the track type and the region, with the highest value of 45% of all Vb cyclones connected to heavy precipitation in summer over the Czech Republic and eastern Austria. In western Germany, Atlantic winter cyclones are most relevant for heavy precipitation. The analysis of the top 50 precipitation events revealed an outstanding heavy precipitation period from 2006 to 2011 in the Czech Republic, but no gradual long‐term change. The findings help better understand spatio‐temporal variability of heavy precipitation in the context of floods and may be used for evaluating climate models.

Hydroclimate variability in Scandinavia over the last millennium – insights from a climate model–proxy data comparison

Abstract. The integration of climate proxy information with general circulation model (GCM) results offers considerable potential for deriving greater understanding of the mechanisms underlying climate variability, as well as unique opportunities for out-of-sample evaluations of model performance. In this study, we combine insights from a new tree-ring hydroclimate reconstruction from Scandinavia with projections from a suite of forced transient simulations of the last millennium and historical intervals from the CMIP5 and PMIP3 archives. Model simulations and proxy reconstruction data are found to broadly agree on the modes of atmospheric variability that produce droughts–pluvials in the region. Despite these dynamical similarities, large differences between simulated and reconstructed hydroclimate time series remain. We find that the GCM-simulated multi-decadal and/or longer hydroclimate variability is systematically smaller than the proxy-based estimates, whereas the dominance of GCM-simulated high-frequency components of variability is not reflected in the proxy record. Furthermore, the paleoclimate evidence indicates in-phase coherencies between regional hydroclimate and temperature on decadal timescales, i.e., sustained wet periods have often been concurrent with warm periods and vice versa. The CMIP5–PMIP3 archive suggests, however, out-of-phase coherencies between the two variables in the last millennium. The lack of adequate understanding of mechanisms linking temperature and moisture supply on longer timescales has serious implications for attribution and prediction of regional hydroclimate changes. Our findings stress the need for further paleoclimate data–model intercomparison efforts to expand our understanding of the dynamics of hydroclimate variability and change, to enhance our ability to evaluate climate models, and to provide a more comprehensive view of future drought and pluvial risks.

Impact of Ice Cloud Microphysics on Satellite Cloud Retrievals and Broadband Flux Radiative Transfer Model Calculations

Ice cloud particles exhibit a range of shapes and sizes affecting a cloud’s single-scattering properties. Because they cannot be inferred from passive visible/infrared imager measurements, assumptions about the bulk single-scattering properties of ice clouds are fundamental to satellite cloud retrievals and broadband radiative flux calculations. To examine the sensitivity to ice particle model assumptions, three sets of models are used in satellite imager retrievals of ice cloud fraction, thermodynamic phase, optical depth, effective height, and particle size, and in top-of-atmosphere (TOA) and surface broadband radiative flux calculations. The three ice particle models include smooth hexagonal ice columns (SMOOTH), roughened hexagonal ice columns, and a two-habit model (THM) comprising an ensemble of hexagonal columns and 20-element aggregates. While the choice of ice particle model has a negligible impact on daytime cloud fraction and thermodynamic phase, the global mean ice cloud optical depth retrieved from THM is smaller than from SMOOTH by 2.3 (28%), and the regional root-mean-square difference (RMSD) is 2.8 (32%). Effective radii derived from THM are 3.9 μm (16%) smaller than SMOOTH values and the RMSD is 5.2 μm (21%). In contrast, the regional RMSD in TOA and surface flux between THM and SMOOTH is only 1% in the shortwave and 0.3% in the longwave when a consistent ice particle model is assumed in the cloud property retrievals and forward radiative transfer model calculations. Consequently, radiative fluxes derived using a consistent ice particle model assumption throughout provide a more robust reference for climate model evaluation compared to ice cloud property retrievals.

Potential to Constrain Projections of Hot Temperature Extremes

Projected changes in temperature extremes, such as regional changes in the intensity and frequency of hot extremes, differ strongly across climate models. This study shows that this disagreement can be partly explained by discrepancies in the representation of the present-day temperature distribution, motivating the evaluation of models with observations. By evaluating climate models on carefully selected metrics, the models that are more likely to be reliable for long-term projections of temperature extremes are identified. The study found that frequencies of hot extremes are likely to increase at a higher rate than the multimodel mean estimate over large parts of the Northern Hemisphere and Australia. This implies that a higher degree of adaptation is required for a given global temperature target. It also found that projected changes in the intensity of hot extremes can be constrained in several regions, including Australia, central North America, and north Asia. In many other regions, large internal variability can often hamper model evaluation. For both aspects—the intensity and the frequency of hot extremes—the total area over which the constraints can be implemented is limited by the quality and completeness of observations. Thereby, this study highlights the importance of long-term, high-quality, and easily accessible observational records for model evaluation, which are vital to ultimately reduce uncertainties in projections of temperature extremes.

Using ARM Observations to Evaluate Climate Model Simulations of Land‐Atmosphere Coupling on the U.S. Southern Great Plains

AbstractSeveral independent measurements of warm‐season soil moisture and surface atmospheric variables recorded at the ARM Southern Great Plains (SGP) research facility are used to estimate the terrestrial component of land‐atmosphere coupling (LAC) strength and its regional uncertainty. The observations reveal substantial variation in coupling strength, as estimated from three soil moisture measurements at a single site, as well as across six other sites having varied soil and land cover types. The observational estimates then serve as references for evaluating SGP terrestrial coupling strength in the Community Atmospheric Model coupled to the Community Land Model. These coupled model components are operated in both a free‐running mode and in a controlled configuration, where the atmospheric and land states are reinitialized daily, so that they do not drift very far from observations. Although the controlled simulation deviates less from the observed surface climate than its free‐running counterpart, the terrestrial LAC in both configurations is much stronger and displays less spatial variability than the SGP observational estimates. Preliminary investigation of vegetation leaf area index (LAI) substituted for soil moisture suggests that the overly strong coupling between model soil moisture and surface atmospheric variables is associated with too much evaporation from bare ground and too little from the vegetation cover. These results imply that model surface characteristics such as LAI, as well as the physical parameterizations involved in the coupling of the land and atmospheric components, are likely to be important sources of the problematical LAC behaviors.

The PMIP4 contribution to CMIP6 – Part 4: Scientific objectives and experimental design of the PMIP4-CMIP6 Last Glacial Maximum experiments and PMIP4 sensitivity experiments

Abstract. The Last Glacial Maximum (LGM, 21 000 years ago) is one of the suite of paleoclimate simulations included in the current phase of the Coupled Model Intercomparison Project (CMIP6). It is an interval when insolation was similar to the present, but global ice volume was at a maximum, eustatic sea level was at or close to a minimum, greenhouse gas concentrations were lower, atmospheric aerosol loadings were higher than today, and vegetation and land-surface characteristics were different from today. The LGM has been a focus for the Paleoclimate Modelling Intercomparison Project (PMIP) since its inception, and thus many of the problems that might be associated with simulating such a radically different climate are well documented. The LGM state provides an ideal case study for evaluating climate model performance because the changes in forcing and temperature between the LGM and pre-industrial are of the same order of magnitude as those projected for the end of the 21st century. Thus, the CMIP6 LGM experiment could provide additional information that can be used to constrain estimates of climate sensitivity. The design of the Tier 1 LGM experiment (lgm) includes an assessment of uncertainties in boundary conditions, in particular through the use of different reconstructions of the ice sheets and of the change in dust forcing. Additional (Tier 2) sensitivity experiments have been designed to quantify feedbacks associated with land-surface changes and aerosol loadings, and to isolate the role of individual forcings. Model analysis and evaluation will capitalize on the relative abundance of paleoenvironmental observations and quantitative climate reconstructions already available for the LGM.