Coupled Model Intercomparison Project 5 Models Research Articles

Seasonal Dependence of Recovery From Surface Cooling Induced by Strong Tropical Volcanic Eruptions

AbstractThis study investigates the response of surface air temperature (SAT) to strong tropical volcanic eruptions (STVEs), focusing on the seasonal anchoring of SAT recovery, using the Coupled Model Intercomparison Project 5 (CMIP5) and CMIP6 climate model outputs. The decrease in SAT was more significant in the Northern Hemisphere than in the Southern Hemisphere. After the STVEs, shortwave radiation at the surface (SW) decreased rapidly and reached a minimum in approximately 6 months, whereas the decrease in SAT lasted for approximately 1 year; thus, the SAT minima lagged behind those of SW. The recovery of SAT from the major minimum SAT after the STVEs occurred in the same seasons, the second boreal autumn and winter. The SAT recovery occurred during the climatologically low solar radiation season after the STVEs. During this season, the decreasing effects of the downward SW (DSRS) by volcanic aerosols could become negligible. The weakening of the decrease in SAT and DSRS had a seasonal cycle in the high‐latitude regions of both hemispheres, which could be a significant factor in the seasonal alignment of returning SATs. Furthermore, the SAT response to the El Niño–Southern Oscillation (ENSO) was examined using composite analysis based on the initial state of ENSO. The influence of ENSO on surface cooling after the STVE was at least 1/5 that of the STVEs and might exceed that of the STVEs, particularly in moderate STVEs. These results were commonly found in the CMIP5 and CMIP6 climate models.

On the Control of Northern Hemispheric Feedbacks by AMOC: Evidence from CMIP and Slab Ocean Modeling

Abstract The climate sensitivity of Earth and the radiative climate feedback both change over time as a result of a so-called “pattern effect,” i.e., changing patterns of surface warming. This is suggested by numerical climate model experiments. The Atlantic meridional overturning circulation (AMOC) influences surface warming patterns as it redistributes energy latitudinally. Thus, this ocean circulation may play an important role for climate-feedback change over time. In this study, two groups of members from the abrupt4xCO2 experiment of phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP) are distinguished: one group showing weak feedback change and the other showing strong feedback change over time. It is found that both groups differ significantly in the AMOC response to 4xCO2. Therefore, experiments with a slab ocean model (SOM) with quadrupling of the CO2 concentration are performed in which the AMOC change is mimicked by changing the ocean heat transport. It is found that in the Northern Hemisphere extratropics the CMIP model group differences can be qualitatively reproduced by the SOM experiments, indicating that the AMOC plays an important role in setting the surface warming pattern. However, in the tropics and especially in the Southern Hemisphere other explanations are necessary.

Climate model Selection by Independence, Performance, and Spread (ClimSIPS v1.0.1) for regional applications

Abstract. As the number of models in Coupled Model Intercomparison Project (CMIP) archives increase from generation to generation, there is a pressing need for guidance on how to interpret and best use the abundance of newly available climate information. Users of the latest CMIP6 seeking to draw conclusions about model agreement must contend with an “ensemble of opportunity” containing similar models that appear under different names. Those who used the previous CMIP5 as a basis for downstream applications must filter through hundreds of new CMIP6 simulations to find several best suited to their region, season, and climate horizon of interest. Here we present methods to address both issues, model dependence and model subselection, to help users previously anchored in CMIP5 to navigate CMIP6 and multi-model ensembles in general. In Part I, we refine a definition of model dependence based on climate output, initially employed in Climate model Weighting by Independence and Performance (ClimWIP), to designate discrete model families within CMIP5 and CMIP6. We show that the increased presence of model families in CMIP6 bolsters the upper mode of the ensemble's bimodal effective equilibrium climate sensitivity (ECS) distribution. Accounting for the mismatch in representation between model families and individual model runs shifts the CMIP6 ECS median and 75th percentile down by 0.43 ∘C, achieving better alignment with CMIP5's ECS distribution. In Part II, we present a new approach to model subselection based on cost function minimization, Climate model Selection by Independence, Performance, and Spread (ClimSIPS). ClimSIPS selects sets of CMIP models based on the relative importance a user ascribes to model independence (as defined in Part I), model performance, and ensemble spread in projected climate outcome. We demonstrate ClimSIPS by selecting sets of three to five models from CMIP6 for European applications, evaluating the performance from the agreement with the observed mean climate and the spread in outcome from the projected mid-century change in surface air temperature and precipitation. To accommodate different use cases, we explore two ways to represent models with multiple members in ClimSIPS, first, by ensemble mean and, second, by an individual ensemble member that maximizes mid-century change diversity within the CMIP overall. Because different combinations of models are selected by the cost function for different balances of independence, performance, and spread priority, we present all selected subsets in ternary contour “subselection triangles” and guide users with recommendations based on further qualitative selection standards. ClimSIPS represents a novel framework to select models in an informed, efficient, and transparent manner and addresses the growing need for guidance and simple tools, so those seeking climate services can navigate the increasingly complex CMIP landscape.

Large spread in the representation of compound long-duration dry and hot spells over Europe in CMIP5

Abstract. Long-duration, sub-seasonal dry spells in combination with high temperature extremes during summer have led to extreme impacts on society and ecosystems in the past. Such events are expected to become more frequent due to increasing temperatures as a result of anthropogenic climate change. However, there is little information on how long-duration dry and hot spells are represented in global climate models (GCMs). In this study, we evaluate 33 CMIP5 (coupled model intercomparison project 5) GCMs in their representation of long-duration dry spells and temperatures during dry spells. We define a dry spell as a consecutive number of days with a daily precipitation of less than 1 mm. CMIP5 models tend to underestimate the persistence of dry spells in northern Europe, while a large variability exists between model estimates in central and southern Europe, where models have contrasting biases. Throughout Europe, we also find a large spread between models in their representation of temperature extremes during dry spells. In central and southern Europe this spread in temperature extremes between models is related to the representation of dry spells, where models that produce longer dry spells also produce higher temperatures, and vice versa. Our results indicate that this variability in model estimates is due to model differences and not internal variability. At latitudes between 50–60∘ N, the differences in the representation of persistent dry spells are strongly related to the representation of persistent anticyclonic systems, such as atmospheric blocking and subtropical ridges. Furthermore, models simulating a higher frequency of anticyclonic systems than ERA5 also simulate temperatures in dry spells that are between 1.4, and 2.8 K warmer than models with a lower frequency in these areas. Overall, there is a large spread between CMIP5 models in their representation of long-duration dry and hot events that is due to errors in the representation of large-scale anticyclonic systems in certain parts of Europe. This information is important to consider when interpreting the plausibility of future projections from climate models and highlights the potential value that improvements in the representation of anticyclonic systems may have for the simulation of impactful hazards.

Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA 1.0) compatible with Coupled Model Intercomparison Project (CMIP) climate data

Abstract. Tropical cyclones are responsible for a large share of global damage resulting from natural disasters, and estimating cyclone-related damage at a national level is a challenge attracting growing interest in the context of climate change. The global climate models, whose outputs are available from the Coupled Model Intercomparison Project (CMIP), do not resolve tropical cyclones. The Cyclone generation Algorithm including a THERmodynamic module for Integrated National damage Assessment (CATHERINA), presented in this paper, couples statistical and thermodynamic relationships to generate synthetic tracks sensitive to local climate conditions and estimates the damage induced by tropical cyclones at a national level. The framework is designed to be compatible with the data from CMIP models offering a reliable solution to resolve tropical cyclones in climate projections. We illustrate this by producing damage projections in representative concentration pathways (RCPs) at the global level and for individual countries. The algorithm contains a module to correct biases in climate models based on the distributions of the climate variables in the reanalyses. This model was primary developed to provide the economic and financial community with reliable signals allowing for a better quantification of physical risks in the long term, to estimate, for example, the impact on sovereign debt.

Evaluation of extreme precipitation climate indices and their projected changes for Brazil: From CMIP3 to CMIP6

Extreme events usually cause numerous economic and social losses, especially in vulnerable countries, such as Brazil. Understanding whether the evolution of Earth System Models (ESMs) from Coupled Model Intercomparison Project (CMIP) improves the representation of extreme events and investigating their future change is fundamental because device policies of adaptation and mitigation to climate change generally consider the results of the most recent generation of ESMs. This study analyzes the performance of a subset of 40 ESMs from CMIP3, CMIP5, and CMIP6 in simulating eight extreme precipitation climate indices over Brazil during 1981–2000 and also estimates their projected changes for the middle (2046–2065) and far future (2081–2 100) under the worst-case scenario for each CMIP generation. Results reveal that CDD are the most challenging precipitation index to be simulated, while the best ones were PRCPTOT and R20mm. The model performance shows that CMIP3 has the best skill for Northeast Brazil, CMIP5 for Center-West, and CMIP6 for North, Southeast and South regions. Thus, at least for Brazil, the evolution of the ESMs from CMIP did not reflect a substantial improvement in the representation of precipitation climate extremes over all Brazilian regions. In addition, all the models across CMIP generations have difficulty in simulating the observed trends. This indicate that improvements are still needed in CMIP models. Despite the relative low performance in the historical climate, the climate projections indicate a consensus signal among most of precipitation climate extremes and CMIP generations, which increase its reliability. Overall, the extreme precipitation events are projected to be more severe, frequent, and long-lasting in all Brazilian regions, with the more pronounce changes expected in heavy rainfall and severe droughts in the central northern portion of Brazil and in the southern sector.

Predictability of sea surface temperature anomalies at the eastern pole of the Indian Ocean Dipole—using a convolutional neural network model

In this study, we train a convolutional neural network (CNN) model using a selection of Coupled Model Intercomparison Project (CMIP) phase 5 and 6 models to investigate the predictability of the sea surface temperature (SST) variability off the Sumatra-Java coast in the tropical southeast Indian Ocean, the eastern pole of the Indian Ocean Dipole (IOD). Results show that the CNN model can beat the persistence of the interannual SST variability, such that the eastern IOD (EIOD) SST variability can be forecast up to 6 months in advance. Visualizing the CNN model using a gradient weighted class activation map shows that the strong positive IOD events (cold EIOD SST anomalies) can stem from different processes: internal Indian Ocean dynamics were associated with the 1994 positive IOD, teleconnection from the equatorial Pacific was important in 1997, and cooling off the Australian coast in the southeast Indian Ocean contributed to the 2019 positive IOD. The CNN model overcomes the winter prediction barrier of the IOD, to a large extent due to the frequent transition from a warm state of the Indian Ocean to a negative IOD condition (warm EIOD SST anomalies) over the boreal winter to the following spring period. The forecasting skills of the CNN model are on par with predictions from a coupled seasonal forecasting model (ACCESS-S2), even outperforming this dynamic model in seasons leading to the IOD peaks. The ability of the CNN model to identify key dynamic drivers of the EIOD SST variability suggests that the CMIP models can capture the internal Indian Ocean variability and its teleconnection with the Pacific climate variability.

Recent progress in simulating two types of ENSO – from CMIP5 to CMIP6

The new emerging type of El Niño brings challenges to the state-of-the-art coupled models to simulated features of El Niño - Southern Oscillation (ENSO) diversity. The Coupled Model Intercomparison Project (CMIP), containing the advanced worldwide coupled models, has recently released the model outputs in phase 6. In this paper, the characteristics of two types of ENSO in 19 models from CMIP phase 5 and their counterparts in phase 6 are assessed regarding the spatial and temporal features and the seasonal cycle features. The weaker amplitude of Eastern Pacific (EP) and Central Pacific (CP) ENSO in CMIP5 is increased and the spatial structure of CP ENSO is better depicted in CMIP6. However, no significant improvement in the ENSO periodicity and the ENSO phase-locking behavior compared to CMIP5. A synthetic ENSO score, containing eight metrics, is defined and employed to evaluate the performance of each CMIP model. The average ENSO score for CMIP6 is 2.375, indicating modest improvement compared to the average score of 2.441 for CMIP5. Furthermore, the slight improvement in the ENSO score is partly related to the reduced climatology bias of sea surface temperature in the Niño4 region. The overall evaluation provides necessary information for future investigation about the mechanism exploration of the ENSO diversity based on the models with better performance.

Uncertainty in Projected Changes in Precipitation Minus Evaporation: Dominant Role of Dynamic Circulation Changes and Weak Role for Thermodynamic Changes

AbstractEnd of century projections from Coupled Model Intercomparison Project (CMIP) models show a decrease in precipitation over subtropical oceans that often extends into surrounding land areas, but with substantial intermodel spread. Changes in precipitation are controlled by both thermodynamical and dynamical processes, though the importance of these processes for regional scales and for intermodel spread is not well understood. The contribution of dynamic and thermodynamic processes to the model spread in regional precipitation minus evaporation (P − E) is computed for 48 CMIP models. The intermodel spread is dominated essentially everywhere by the change of the dynamic term, including in most regions where thermodynamic changes drive the multi‐model mean response. The dominant role of dynamic changes is insensitive to zonal averaging which removes any influence of stationary wave changes, and is also evident in subtropical oceanic regions. Relatedly, intermodel spread in P − E is generally unrelated to climate sensitivity.

The ICON Earth System Model Version 1.0

AbstractThis work documents the ICON‐Earth System Model (ICON‐ESM V1.0), the first coupled model based on the ICON (ICOsahedral Non‐hydrostatic) framework with its unstructured, icosahedral grid concept. The ICON‐A atmosphere uses a nonhydrostatic dynamical core and the ocean model ICON‐O builds on the same ICON infrastructure, but applies the Boussinesq and hydrostatic approximation and includes a sea‐ice model. The ICON‐Land module provides a new framework for the modeling of land processes and the terrestrial carbon cycle. The oceanic carbon cycle and biogeochemistry are represented by the Hamburg Ocean Carbon Cycle module. We describe the tuning and spin‐up of a base‐line version at a resolution typical for models participating in the Coupled Model Intercomparison Project (CMIP). The performance of ICON‐ESM is assessed by means of a set of standard CMIP6 simulations. Achievements are well‐balanced top‐of‐atmosphere radiation, stable key climate quantities in the control simulation, and a good representation of the historical surface temperature evolution. The model has overall biases, which are comparable to those of other CMIP models, but ICON‐ESM performs less well than its predecessor, the Max Planck Institute Earth System Model. Problematic biases are diagnosed in ICON‐ESM in the vertical cloud distribution and the mean zonal wind field. In the ocean, sub‐surface temperature and salinity biases are of concern as is a too strong seasonal cycle of the sea‐ice cover in both hemispheres. ICON‐ESM V1.0 serves as a basis for further developments that will take advantage of ICON‐specific properties such as spatially varying resolution, and configurations at very high resolution.

Minimal CMIP Emulator (MCE v1.2): a new simplified method for probabilistic climate projections

Abstract. Climate model emulators have a crucial role in assessing warming levels of many emission scenarios from probabilistic climate projections based on new insights into Earth system response to CO2 and other forcing factors. This article describes one such tool, MCE, from model formulation to application examples associated with a recent model intercomparison study. The MCE is based on impulse response functions and parameterized physics of effective radiative forcing and carbon uptake over ocean and land. Perturbed model parameters for probabilistic projections are generated from statistical models and constrained with a Metropolis–Hastings independence sampler. Some of the model parameters associated with CO2-induced warming have a covariance structure, as diagnosed from complex climate models of the Coupled Model Intercomparison Project (CMIP). Perturbed ensembles can cover the diversity of CMIP models effectively, and they can be constrained to agree with several climate indicators such as historical warming. The model's simplicity and resulting successful calibration imply that a method with less complicated structures and fewer control parameters offers advantages when building reasonable perturbed ensembles in a transparent way. Experimental results for future scenarios show distinct differences between CMIP-consistent and observation-consistent ensembles, suggesting that perturbed ensembles for scenario assessment need to be properly constrained with new insights into forced response over historical periods.

Spatiotemporal Trend Analysis of Temperature and Rainfall over Ziway Lake Basin, Ethiopia

Rainfall and temperature trends detection is vital for water resources management and decision support systems in agro-hydrology. This study assessed the historical (1983–2005) and future (2026–2100) rainfall, maximum temperature (Tmax), and minimum temperature (Tmin) trends of the Ziway Lake Basin (Ethiopia). The daily observed rainfall and temperature data at eleven stations were obtained from the National Meteorological Agency (NMA) of Ethiopia, while simulated historical and future climate data were obtained from the Coupled Model Intercomparison Project 5 (CMIP5) datasets under Representative Concentration Pathways (RCP) of 4.5 and 8.5. The CMIP5 datasets were statistically downscaled by using the climate model data for hydrologic modeling (CMhyd) tool and bias corrected using the distribution mapping method available in the CMhyd tool. The performance of simulated rainfall, Tmax, and Tmin of the CMIP5 models were statistically evaluated using observation datasets at eleven stations. The results showed that the selected CMIP5 models can reasonably simulate the monthly rainfall, Tmax, and Tmin at the majority of the stations. Modified Mann–Kendall trend test were applied to estimate the trends of annual rainfall, Tmax, and Tmin in the historical and future periods. We found that rainfall experienced no clear trends, while Tmax, and Tmin showed consistently significant increasing trends under both RCP 4.5 and 8.5 scenarios. However, the warming is expected to be greater under RCP 8.5 than RCP 4.5 by the end of the 21st century, resulting in an increasing trend of Tmax and Tmin at all stations. The greatest warming occurred in the central part of the basin, with statistically significant increases largely seen by the end of the 21st century, which is expected to exacerbate the evapotranspiration demand of the area that could negatively affect the freshwater availability within the basin. This study increases our understanding of historic trends and projected future change effects on rainfall- and evapotranspiration-related climate variables, which can be used to inform adaptive water resource management strategies.

Snow Reconciles Observed and Simulated Phase Partitioning and Increases Cloud Feedback

AbstractThe surprising increase of Earth's climate sensitivity in the most recent coupled model intercomparison project (CMIP) models has been largely attributed to extratropical cloud feedback, which is thought to be driven by greater supercooled water in present‐day cloud phase partitioning (CPP). Here, we report that accounting for precipitation in the Goddard Institute for Space Studies ModelE3 radiation scheme, neglected in more than 60% of CMIP6 and 90% of CMIP5 models, systematically changes its apparent CPP and substantially increases its cloud feedback, consistent with results using CMIP models. Including precipitation in the comparison with cloud–aerosol lidar and infrared pathfinder satellite observations (CALIPSO) measurements and in model radiation schemes is essential to faithfully constrain cloud amount and phase partitioning, and simulate cloud feedbacks. Our findings suggest that making radiation schemes precipitation‐aware (missing in most CMIP6 models) should strengthen their positive cloud feedback and further increase their already high mean climate sensitivity.

A diagnostic model for the large-scale tropical circulation based on moist static energy balance

In this study we present a diagnostic model for the large-scale tropical circulation (vertical motion) based on the moist static energy equation for first baroclinic mode anomalies (MSEB model). The aim of this model is to provide a basis for conceptual understanding of the drivers of the large-scale tropical circulation changes or variations as they are observed or simulated in Coupled Model Inter-comparison Project (CMIP) models. The MSEB model is based on previous studies relating vertical motion in the tropics to the driving forces of the tropospheric column heating rate, advection of moisture and heat, and the moist stability of the air columns scaled by the first baroclinic mode. We apply and evaluate the skill of this model on the basis of observations (reanalysis) and CMIP model simulations of the large-scale tropical vertical motion. The model is capable of diagnosing the large-scale pattern of vertical motion of the mean state, annual cycle, interannual variability, model-to-model variations and in warmer climates of climate change scenarios; it has spatial correlations of 0.6–0.8 and nearly unbiased amplitudes for the whole tropics (30° S–30° N). The skills are generally better over oceans at large scales and worse over land regions. For the interannual variation of zonally anomalous and zonal mean circulation in tropical Pacific region, it has temporal correlations ~ 0.8. The model also tends to have an upward motion bias at higher latitudes, but still has good correlations in temporal and spatial variations even at the higher latitudes. It is further illustrated how the MSEB model sensitivities can be used to determine the mechanisms in the models that are responsible for the mean state, seasonal cycle and interannual variability such as El Nino. The model clearly illustrates that the seasonal cycle in the circulation is driven by the incoming solar radiation and that the El Nino shift in the Walker circulation results mainly from the sea-surface temperature changes. Overall, the model provides a powerful diagnostic tool to understand tropical circulation change on larger and longer (> month) time scales.

Stationarity in the variability of arid precipitation: A case study of arid Central Asia

Precipitation is particularly scarce in arid Central Asia (CA), and is expected to be severely impacted by future warming, and the assessment of the stationarity of precipitation variability is important for managing surface water resources in this region. In this study, we investigated the statistics of stationarity in the totals and extremes of precipitation in CA based on the longest observational records (1881–2006), tree-ring reconstructed records (1756–2012 and 1760–2015), and the Coupled Model Intercomparison Project 5 (CMIP5) simulations, applying the autocorrelation function and testing criteria established based on the statistical definitions of stationarity. We analyzed the longest daily precipitation record (Tashkent station, 1881–2006) and found that the autocorrelation coefficient of the precipitation totals (PRCPTOT) and annual maximum 1-day precipitation amount (Rx1day) were statistically insignificant for all lags, implying stationary behavior. Regionally, nearly all the Global Historical Climatology Network-Daily Database (GHCN-D) observatory sites (1925–2005) indicated likely stationary behavior. The reconstructed records were also indistinguishable from a random process. For the CMIP5 models, the simulated and projected PRCPTOT closely approximated a purely random process; however, the projected Rx1day maintained non-stationary means in most of the models under the representative concentration pathway (RCPs), implying that extreme events would increase in the future. The mean precipitation changes (ΔP) can be expressed as an exponential function depending on the length of the successive mean periods (μ) and variance (σ2). The ΔP of the next decade is projected to be within ±14.8% of the previous decades mean annual PRCPTOT over CA. The higher the RCPs, the higher the ΔP over CA. The results show that the detection and prediction of precipitation change will be challenging in arid CA.

Reduced Complexity Model Intercomparison Project Phase 1: introduction and evaluation of global-mean temperature response

Abstract. Reduced-complexity climate models (RCMs) are critical in the policy and decision making space, and are directly used within multiple Intergovernmental Panel on Climate Change (IPCC) reports to complement the results of more comprehensive Earth system models. To date, evaluation of RCMs has been limited to a few independent studies. Here we introduce a systematic evaluation of RCMs in the form of the Reduced Complexity Model Intercomparison Project (RCMIP). We expect RCMIP will extend over multiple phases, with Phase 1 being the first. In Phase 1, we focus on the RCMs' global-mean temperature responses, comparing them to observations, exploring the extent to which they emulate more complex models and considering how the relationship between temperature and cumulative emissions of CO2 varies across the RCMs. Our work uses experiments which mirror those found in the Coupled Model Intercomparison Project (CMIP), which focuses on complex Earth system and atmosphere–ocean general circulation models. Using both scenario-based and idealised experiments, we examine RCMs' global-mean temperature response under a range of forcings. We find that the RCMs can all reproduce the approximately 1 ∘C of warming since pre-industrial times, with varying representations of natural variability, volcanic eruptions and aerosols. We also find that RCMs can emulate the global-mean temperature response of CMIP models to within a root-mean-square error of 0.2 ∘C over a range of experiments. Furthermore, we find that, for the Representative Concentration Pathway (RCP) and Shared Socioeconomic Pathway (SSP)-based scenario pairs that share the same IPCC Fifth Assessment Report (AR5)-consistent stratospheric-adjusted radiative forcing, the RCMs indicate higher effective radiative forcings for the SSP-based scenarios and correspondingly higher temperatures when run with the same climate settings. In our idealised setup of RCMs with a climate sensitivity of 3 ∘C, the difference for the ssp585–rcp85 pair by 2100 is around 0.23∘C(±0.12 ∘C) due to a difference in effective radiative forcings between the two scenarios. Phase 1 demonstrates the utility of RCMIP's open-source infrastructure, paving the way for further phases of RCMIP to build on the research presented here and deepen our understanding of RCMs.

The ARM Data-Oriented Metrics and Diagnostics Package for Climate Models: A New Tool for Evaluating Climate Models with Field Data

AbstractThe U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud, and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Intercomparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.

Observations for Model Intercomparison Project (Obs4MIPs): status for CMIP6

Abstract. The Observations for Model Intercomparison Project (Obs4MIPs) was initiated in 2010 to facilitate the use of observations in climate model evaluation and research, with a particular target being the Coupled Model Intercomparison Project (CMIP), a major initiative of the World Climate Research Programme (WCRP). To this end, Obs4MIPs (1) targets observed variables that can be compared to CMIP model variables; (2) utilizes dataset formatting specifications and metadata requirements closely aligned with CMIP model output; (3) provides brief technical documentation for each dataset, designed for nonexperts and tailored towards relevance for model evaluation, including information on uncertainty, dataset merits, and limitations; and (4) disseminates the data through the Earth System Grid Federation (ESGF) platforms, making the observations searchable and accessible via the same portals as the model output. Taken together, these characteristics of the organization and structure of obs4MIPs should entice a more diverse community of researchers to engage in the comparison of model output with observations and to contribute to a more comprehensive evaluation of the climate models. At present, the number of obs4MIPs datasets has grown to about 80; many are undergoing updates, with another 20 or so in preparation, and more than 100 are proposed and under consideration. A partial list of current global satellite-based datasets includes humidity and temperature profiles; a wide range of cloud and aerosol observations; ocean surface wind, temperature, height, and sea ice fraction; surface and top-of-atmosphere longwave and shortwave radiation; and ozone (O3), methane (CH4), and carbon dioxide (CO2) products. A partial list of proposed products expected to be useful in analyzing CMIP6 results includes the following: alternative products for the above quantities, additional products for ocean surface flux and chlorophyll products, a number of vegetation products (e.g., FAPAR, LAI, burned area fraction), ice sheet mass and height, carbon monoxide (CO), and nitrogen dioxide (NO2). While most existing obs4MIPs datasets consist of monthly-mean gridded data over the global domain, products with higher time resolution (e.g., daily) and/or regional products are now receiving more attention. Along with an increasing number of datasets, obs4MIPs has implemented a number of capability upgrades including (1) an updated obs4MIPs data specifications document that provides additional search facets and generally improves congruence with CMIP6 specifications for model datasets, (2) a set of six easily understood indicators that help guide users as to a dataset's maturity and suitability for application, and (3) an option to supply supplemental information about a dataset beyond what can be found in the standard metadata. With the maturation of the obs4MIPs framework, the dataset inclusion process, and the dataset formatting guidelines and resources, the scope of the observations being considered is expected to grow to include gridded in situ datasets as well as datasets with a regional focus, and the ultimate intent is to judiciously expand this scope to any observation dataset that has applicability for evaluation of the types of Earth system models used in CMIP.

Assessing future water stress scenarios over six nuclear power plant locations of India through downscaled CMIP5 models

The present study has assessed the possible water stress scenarios over six nuclear power plant locations (inland plants at Kakrapar, Kota, and Narora and coastal plants at Kodankulam, Kalpakkam, and Tarapur) of India based on downscaled climatic products from 12 coupled model inter-comparison project 5 (CMIP5) simulations. Firstly, statistically downscaled scenarios over power plant locations for water temperature, precipitation, evapotranspiration, and sea surface temperature (SST) have been developed using various statistical downscaling methods. Secondly, the water stress has been quantified by formulating a multivariate standardized water stress index (MSWSI) based on water temperature and freshwater availability (precipitation minus evapotranspiration) over inland plants and a univariate index from the SST for coastal plants. Results have indicated that three inland power plants are not expecting any scarcity of freshwater availability. However, they have been projected to face high to severe water stress from middle to end of the century due to a higher warming rate of water temperature under global warming conditions. Similarly, three coastal plants have also been projected to prevail high to severe water stress through enhanced SST warming. Therefore, the efficiency and productivity of the nuclear plants may reduce under changing climatic conditions.

QBO‐MJO Connection in CMIP5 Models

AbstractRecent studies have shown that the quasi‐biennial oscillation (QBO) affects the boreal winter Madden‐Julian oscillation (MJO), its teleconnection, and even its subseasonal prediction skill. Despite its widespread influences, the QBO‐MJO connection has rarely been examined with a climate model. Here, we demonstrate that the QBO‐MJO connection is not well reproduced in the four Coupled Model Intercomparison Project 5 (CMIP5) models that internally generate the QBO. A weak hint of the QBO‐MJO connection is found only in the medium‐resolution Max Planck Institute Earth System Model (MPI‐ESM‐MR). The MJO response to the QBO in this model, however, is not robust and is less organized than that in the observations. The reasons for no or weak QBO‐MJO connection in the climate model simulations are discussed in terms of the underestimated QBO and MJO amplitudes and weak static stability change in response to the QBO in the model.