Reply on RC5

Outputs from climate models are the primary data source in climate change impact studies. However, their interpretation is not straightforward. In recent years, several methods have been developed in order to quantify the uncertainty in climate projections. One of the common assumptions in almost all these methods is that the climate models are independent. This study addresses the validity of this assumption for two ensembles of regional climate models (RCMs) from the Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) project based on the land cells covering Denmark. Daily precipitation indices from an ensemble of RCMs driven by the 40-yr ECMWF Re-Analysis (ERA-40) and an ensemble of the same RCMs driven by different general circulation models (GCMs) are analyzed. Two different methods are used to estimate the amount of independent information in the ensembles. These are based on different statistical properties of a measure of climate model error. Additionally, a hierarchical cluster analysis is carried out. Regardless of the method used, the effective number of RCMs is smaller than the total number of RCMs. The estimated effective number of RCMs varies depending on the method and precipitation index considered. The results also show that the main cause of interdependency in the ensemble is the use of the same RCM driven by different GCMs. This study shows that the precipitation outputs from the RCMs in the ENSEMBLES project cannot be considered independent. If the interdependency between RCMs is not taken into account, the uncertainty in the RCM simulations of current regional climate may be underestimated. This will in turn lead to an underestimation of the uncertainty in future precipitation projections.

Regional climate models (RCMs) are dynamical downscaling tools aimed to improve the modelling of local physical processes. Ensembles of RCMs are widely used to improve the coarse-grain estimates of global climate models (GCMs) since the use of several RCMs helps to palliate uncertainties arising from different dynamical cores and numerical schemes methods. In this paper, we analyse the differences and similarities in the climate change response for an ensemble of heterogeneous RCMs forced by one GCM (HadAM3H), and one emissions scenario (IPCC’s SRES-A2 scenario). As a difference with previous approaches using PRUDENCE database, the statistical description of climate characteristics is made through the spatial and temporal aggregation of the RCMs outputs into probability distribution functions (PDF) of monthly values. This procedure is a complementary approach to conventional seasonal analyses. Our results provide new, stronger evidence on expected marked regional differences in Europe in the A2 scenario in terms of precipitation and temperature changes. While we found an overall increase in the mean temperature and extreme values, we also found mixed regional differences for precipitation.

The Met Office Hadley Centre's PRECIS regional climate modelling system has been used to generate a five member ensemble of climate projections for Africa over the 50 km resolution Coordinated Regional climate Downscaling Experiment-Africa domain. The ensemble comprises the downscaling of a subset of the Hadley Centre's perturbed physics global climate model (GCM) ensemble chosen to exclude ensemble members unable to represent the African climate realistically and then to capture the spread in outcomes from the projections of the remaining models. The PRECIS simulations were run from December 1949 to December 2100. The regional climate model (RCM) ensemble captures the annual cycle of temperatures well both for Africa as a whole and the sub-regions. It slightly overestimates precipitation over Africa as a whole and captures the annual cycle of rainfall for most of the African regions. The RCM ensemble substantially improve the patterns and magnitude of precipitation simulation compared to their driving GCM which is particularly noticeable in the Sahel for both the magnitude and timing of the wet season. Present-day simulations of the RCM ensemble are more similar to each other than those of the driving GCM ensemble which indicates that their climatologies are influenced significantly by the RCM formulation and less so by their driving GCMs. Consistent with this, the spread and magnitudes of the large-scale responses of the RCMs are often different than the driving GCMs and arguably more credible given the improved performance of the RCM. This also suggests that local climate forcing will be a significant driver of the regional response to climate change over Africa.

The study examines how regional climate models (RCMs) reproduce the diurnal temperature range (DTR) in their control simulations over Central Europe. We evaluate 30-year runs driven by perfect boundary conditions (the ERA40 reanalysis, 1961–1990) and a global climate model (ECHAM5) of an ensemble of RCMs with 25-km resolution from the ENSEMBLES project. The RCMs’ performance is compared against the dataset gridded from a high-density stations network. We find that all RCMs underestimate DTR in all seasons, notwithstanding whether driven by ERA40 or ECHAM5. Underestimation is largest in summer and smallest in winter in most RCMs. The relationship of the models’ errors to indices of atmospheric circulation and cloud cover is discussed to reveal possible causes of the biases. In all seasons and all simulations driven by ERA40 and ECHAM5, underestimation of DTR is larger under anticyclonic circulation and becomes smaller or negligible for cyclonic circulation. In summer and transition seasons, underestimation tends to be largest for the southeast to south flow associated with warm advection, while in winter it does not depend on flow direction. We show that the biases in DTR, which seem common to all examined RCMs, are also related to cloud cover simulation. However, there is no general tendency to overestimate total cloud amount under anticyclonic conditions in the RCMs, which suggests the large negative bias in DTR for anticyclonic circulation cannot be explained by a bias in cloudiness. Errors in simulating heat and moisture fluxes between land surface and atmosphere probably contribute to the biases in DTR as well.

Assessment of future water availability from the Himalayan watersheds of Indus Basin (Jhelum, Kabul and upper Indus basin—UIB) is a growing concern for safeguarding the sustainable socioeconomic wellbeing downstream. This requires, before all, robust climate change information from the present-day state-of-the-art climate models. However, the robustness of climate change projections highly depends upon the fidelity of climate modeling experiments. Hence, this study assesses the fidelity of seven dynamically refined (0.44^{circ }) experiments, performed under the framework of the coordinated regional climate downscaling experiment for South Asia (CX-SA), and additionally, their six coarse-resolution driving datasets participating in the coupled model intercomparison project phase 5 (CMIP5). We assess fidelity in terms of reproducibility of the observed climatology of temperature and precipitation, and the seasonality of the latter for the historical period (1971–2005). Based on the model fidelity results, we further assess the robustness or uncertainty of the far future climate (2061–2095), as projected under the extreme-end warming scenario of the representative concentration pathway (RCP) 8.5. Our results show that the CX-SA and their driving CMIP5 experiments consistently feature low fidelity in terms of the chosen skill metrics, suggesting substantial cold (6–10 ^{circ }C) and wet (up to 80%) biases and underestimation of observed precipitation seasonality. Surprisingly, the CX-SA are unable to outperform their driving datasets. Further, the biases of CX-SA and of their driving CMIP5 datasets are higher in magnitude than their projected changes under RCP8.5—and hence under less extreme RCPs—by the end of 21st century, indicating uncertain future climates for the Indus Basin watersheds. Higher inter-dataset disagreements of both CMIP5 and CX-SA for their simulated historical precipitation and for its projected changes reinforce uncertain future wet/dry conditions whereas the CMIP5 projected warming is less robust owing to higher historical period uncertainty. Interestingly, a better agreement among those CX-SA experiments that have been obtained through downscaling different CMIP5 experiments with the same regional climate model (RCM) indicates the RCMs’ ability of modulating the influence of lateral boundary conditions over a large domain. These findings, instead of suggesting the usual skill-based identification of ’reasonable’ global or regional low fidelity experiments, rather emphasize on a paradigm shift towards improving their fidelity by exploiting the potential of meso-to-local scale climate models—preferably of those that can solely resolve global-to-local scale climatic processes—in terms of microphysics, resolution and explicitly resolved convections. Additionally, an extensive monitoring of the nival regime within the Himalayan watersheds will reduce the observational uncertainty, allowing for a more robust fidelity assessment of the climate modeling experiments.

This study analyses the length and onset of the four seasons based on the annual climatic cycle of maximum and minimum temperatures. Previous studies focused over climatically homogeneous mid-high latitude areas, employing fixed temperature thresholds (related to climatic features such as freezing point) that can be inadequate when different climate conditions are present. We propose a method related to the daily minimum and maximum temperature 25th and 75th point-dependent climatic percentiles. It is applied to an ensemble of regional climate models (RCMs) of 25-km horizontal resolution over the peninsular Spain and Balearic Islands, where a large variety of climatic regimes, from alpine to semi-desertic conditions, are present. First, baseline climate (1961–2000) ERA40-forced RCM simulations are successfully compared with the Spain02 daily observational database, following astronomical season length (around 90 days). This result confirms the validity of the proposed method and capability of the RCMs to describe the seasonal features. Future climate global climate model-forced RCMs (2071–2100) compared with present climate (1961–1990) simulations indicate the disappearance of winter season, a summer enlargement (onset and end) and a slight spring and autumn increase.

Statistical downscaling is a computationally efficient approach in producing a large ensemble of fine-resolution future climate projections to support local climate adaptation efforts. Here, we propose a deep learning–based framework, named BC_DLD, to downscale the Coupled Model Intercomparison Project phase 6 (CMIP6) Earth system model (ESM) outputs from the coarse native resolution to a locally relevant fine resolution at kilometer scale and apply it to the Northeast United States, a region experiencing rapid increase of heavy precipitation and in great need for locally actionable climate information. Employing a two-phase multivariate approach by utilizing an empirical-quantile delta mapping (E-QDM) approach for bias correction at a coarse resolution and deep convolutional neural networks (CNNs) for downscaling the corrected data to a finer spatial resolution, BC_DLD is designed to preserve the spatial characteristics from observations while retaining the temporal characteristics and climate change signals from ESMs. Here, we demonstrate the implementation of BC_DLD by downscaling the outputs of GFDL-ESM4 historical run and future shared socioeconomic pathway (SSP) 5–8.5 scenario over the Northeast, from ∼100 to ∼25 km [using fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) as reference] and then further down to ∼6 km (using Livneh data as reference), and compare the downscaled data with an existing product, localized constructed analogs, version 2 (LOCA2). Despite using the same observational training data as LOCA2, BC_DLD produces more intense and rapid future increase of extreme precipitation than LOCA2, alleviating the underestimation of extremes common among statistical downscaling products. The emergent relationship between extreme precipitation intensity and temperature in BC_DLD more closely resembles the observations, further establishing its efficacy in producing useful and usable future climate projections.

Projected future regional climate changes in Luxembourg are assessed based on a six-member ensemble of regional climate models (RCM) from the ENSEMBLES project. The key aspects are projected changes in air temperature and their impacts on vegetation. Up to now, there have been only few assessments of future climate conditions for Luxembourg. As agriculture is the dominant land use in Luxembourg, possible effects on crops and vegetation in general are highly relevant. Different RCMs at 25 km spatial and a daily temporal resolution, ranging from 1961 to 2100 based on the SRES A1B emission scenario are used. To reduce systematic biases in the RCM-derived time series, a bias correction is applied. Multi-model annual mean temperatures are projected to increase by 3.1 °C between the reference time span (1961 to 1990) and the far future (2069 to 2098). Clear change signals are found in seasonal bivariate frequency distributions of air temperature and precipitation. Derived impacts are an elongation of the thermal vegetation period by 6.2 days per decade due to an earlier onset in spring; growing degree day sums show a substantial increase leading to potentially better growth conditions; the earlier onset of the vegetation period causes an increase in late frost risk, especially in the near future (2021 to 2050) projections compared to the reference period.

<p>Heavy precipitation is a major natural hazard that can have severe impacts.  In response to global warming, the character of heavy precipitation is expected to change. Projections of the future hydrologic cycle, especially of heavy precipitation, are uncertain. Especially at the regional scale, different data sources, such as different ensembles of global and regional climate models (GCMs and RCMs), provide sometimes conflicting conclusions. Therefore, it is even more important to investigate where differences between ensembles lie and to which processes they can be attributed.</p><p>A precipitation scaling (introduced by Paul O’Gorman) is used to disentangle thermodynamic and dynamic contributions in extreme precipitation. In this work, we compare the results of CMIP5 and CMIP6 and focus on climate change signals between the periods 1971-2000 and 2071-2100 over Europe. The thermodynamic component provides homogeneous signals across Europe with a rise in extreme precipitation of about 7 %/K. In contrast, the dynamic component shows no spatial homogeneous results where the dynamic contribution can even modify the thermodynamic signal. The spread between the models within one ensemble is much larger. However, based on initial analyses, the spread in the CMIP6 models appears to have become smaller compared to CMIP5. This means, understanding the dynamic changes is the key to understanding the differences between the ensembles.</p><p>As a next step, to analyze the discrepancy between CMIP5 and CMIP6 in terms of atmospheric circulation changes, we look into three atmospheric drivers: tropical and polar amplification of global warming and changes in stratospheric vortex strength.  </p>

The study examines links of summer heat waves and winter cold spells in Central Europe to atmospheric circulation and specifically its persistence in an ensemble of regional climate models (RCMs). We analyse 13 RCMs driven by the ERA-40 reanalysis and compare them against observations over reference period 1971–2000. Using objective classification of circulation types and an efficiency coefficient with a block resampling test, we identify circulation types significantly conducive to heat waves and cold spells. We show that the RCMs have a stronger tendency to group together days with very high or low temperature and tend to simulate too many heat waves and cold spells, especially those lasting 5 days and more. Circulation types conducive to heat waves in summer are characterized by anticyclonic, southerly and easterly flow, with increasing importance of warm advection during heat waves. Winter cold spells are typically associated with easterly and anticyclonic flow, and the onset of cold spells tends to be linked to northerly and cyclonic flow with cold advection. The RCMs are generally able to reproduce the links between circulation and heat waves or cold spells, including the radiation-to-advection effect for heat waves and the opposite advection-to-radiation effect for cold spells. They capture relatively well also changes of mean temperature anomalies during sequences of given circulation types, namely the tendency towards temperature increase (decrease) during those types conducive to heat waves (cold spells). Since mean lengths of all circulation supertypes are overestimated in the RCMs, we conclude that the overly persistent circulation in climate models contributes to the overestimated frequency of long heat waves and cold spells. As these biases are rather general among the examined RCMs and similar drawbacks are likely to be manifested in climate model simulations for the twenty-first century, the results also suggest that climate change scenarios for spells of days with high or low temperatures should be interpreted with a view to the models’ limitations in simulating the recent climate.

Coupled Model Intercomparison Project Phase 6 (CMIP6) data sets: These data includes all datasets published for 'CMIP6.RFMIP.MIROC.MIROC6.piClim-histaer' according to the Data Reference Syntax defined as 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The Earth System Model MIROC6, released in 2017, includes the components: aerosol: SPRINTARS6.0, atmos: CCSR AGCM (T85; 256 x 128 longitude/latitude; 81 levels; top level 0.004 hPa), land: MATSIRO6.0, ocean: COCO4.9 (tripolar primarily 1deg; 360 x 256 longitude/latitude; 63 levels; top grid cell 0-2 m), seaIce: COCO4.9. The model was run by the JAMSTEC (Japan Agency for Marine-Earth Science and Technology, Kanagawa 236-0001, Japan), AORI (Atmosphere and Ocean Research Institute, The University of Tokyo, Chiba 277-8564, Japan), NIES (National Institute for Environmental Studies, Ibaraki 305-8506, Japan), and R-CCS (RIKEN Center for Computational Science, Hyogo 650-0047, Japan) (MIROC) in native nominal resolutions: aerosol: 100 km, atmos: 250 km, land: 100 km, ocean: 100 km, seaIce: 100 km. Project: These data have been generated as part of the internationally-coordinated Coupled Model Intercomparison Project Phase 6 (CMIP6; see also GMD Special Issue: http://www.geosci-model-dev.net/special_issue590.html). The simulation data provides a basis for climate research designed to answer fundamental science questions, and the results will undoubtedly be relied on by authors of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC-AR6). CMIP6 is a project coordinated by the Working Group on Coupled Modelling (WGCM) as part of the World Climate Research Programme (WCRP). Phase 6 builds on previous phases executed under the leadership of the Program for Climate Model Diagnosis and Intercomparison (PCMDI) and relies on the Earth System Grid Federation (ESGF) and the Centre for Environmental Data Analysis (CEDA) along with numerous related activities for implementation. The original data is hosted and partially replicated at a federated collection of data nodes, and most of the data relied on by the IPCC is being archived for long-term preservation at the IPCC Data Distribution Centre (IPCC DDC) hosted by World Data Centre for Climate (WDCC) at DKRZ. The project includes simulations from about 90 global climate models and around 40 institutions and organizations worldwide. - Project website: https://pcmdi.llnl.gov/CMIP6.

Different patterns of climate change scenarios for short-term and multi-day precipitation extremes in the Mediterranean

The Mediterranean basin is a region characterized by its vulnerability to changes in the water cycle. Hence, the impact of global warming on the water resources in the Mediterranean zone is one of the major concerns for the scientific community. The future climate projections used to elaborate the IPCC report of 2007 show great alterations in the evaporation and precipitation over the Mediterranean Sea at the end of 21st century. In this work we investigate the changes in the Mediterranean Sea water budget by using SRES‐A1B scenario experiments performed with high resolution (25 km) regional climate models (RCMs). The RCMs provide good estimates of the water budget components, in particular with a significant improvement of the runoff and Black Sea discharge terms compared to the coarser resolution general circulation models (GCMs) used in the last Intergovernmental Panel of Climate Change (IPCC) report. As for the case of GCMs, the RCMs show that the Mediterranean water budget is likely to be significantly altered at the end of 21st century. The response of the hydrological variables to global warming starts to be statistically significant from 2050, though some alterations are already observed before 2050. The RCMs predict an increase of evaporation, and a decrease of precipitation, and river and Black Sea discharge, yielding to a large increase of the Mediterranean fresh water deficit. The freshwater deficit for the period 2070–2099 related to 1950–1999 presents a mean increase of +40% for both RCMs and GCMs.

Snow is important for many physical, social, and economic sectors in North America. In a warming climate, the characteristics of snow will likely change in fundamental ways, therefore compelling societal need for future projections of snow. However, many stakeholders require climate change information at finer resolutions that global climate models (GCMs) can provide. The North American Coordinated Regional Downscaling Experiment (NA-CORDEX) provides an ensemble of regional climate model (RCMs) simulations at two resolutions (~ 0.5° and ~ 0.25°) designed to help serve the climate impacts and adaptation communities. This is the first study to examine the differences in end of twenty-first-century projections of snow from the NA-CORDEX RCMs and their driving GCMs. We find that the broad patterns of change are similar across RCMs and GCMs: snow cover retreats, snow mass decreases everywhere except at high latitudes, and the duration of the snow covered season decreases. Regionally, the spatial details, magnitude, percent, and uncertainty of future changes vary between the GCM and RCM ensemble but are similar between the two resolutions of the RCM ensembles. An increase in winter snow amounts at high latitudes is a robust response across all ensembles. Percent snow losses are found to be more substantial in the GCMs than the RCMs over most of North America, especially in regions with high-elevation topography. Specifically, percent snow losses decrease with increasing elevation as the model resolution becomes finer.

This study aims to give a brief overview of an ensemble of regional climate model (RCM) simulations with bias correction for daily precipitation for the Carpathian Region located in Central/Eastern Europe. Within the international initiation called the COordinated Regional Downscaling Experiment (CORDEX) EURO-CORDEX and Med-CORDEX provide RCM simulations targeting Europe as a whole or in a part at the grid resolutions of 0.44o (~50 km) and 0.11o (~12 km). The ensemble of RCMs provides a huge amount of data, which are however prone to biases compared to high resolution observations. Percentile-based bias corrections of the daily precipitation output of EURO-CORDEX and Med-CORDEX RCM ensemble at a common 0.11o × 0.11o horizontal grid resolution are performed based on the high-resolution, high-quality observational dataset: CARPATCLIM. The region covered by the CARPATCLIM dataset can be considered as the Carpathian Region for which the RCM ensemble (consisting of total six members at 0.11o resolution) of a historical period (1976-2005) and under the Representative Concentration Pathway 8.5 over two future time slices (2021-2050 and 2070-2099) are assessed. The present study focuses on precipitation climate indices over the entire Carpathian Region and two subregions representing high- and lowlands within the target region. The analyzed indices are: frequency of rainy days (RR1, days with a total rainfall of at least 1 mm), heavy precipitation days (RR10, days with a total rainfall of at least 10 mm), highest daily precipitation (RX1), maximum consecutive dry days (CDD, the duration of the longest period with < 1 mm total daily precipitation), maximum consecutive wet periods (CWD, the duration of the longest period with > 1 mm total daily precipitation). The key findings are as follows: (1) the influence of orography on seasonal precipitation change is evident throughout the analyzed periods, (2) precipitation change holds an unequivocal seasonality as most models estimate significant winter (DJF) precipitation increase (~30%), while in general drier (~20%) summers (JJA) are projected over most part of the Carpathian Region by the end of the 21st century, (3) over the subregions RR10 shows a clear signal of increase in all seasons (10-50%), except summer (~-15%), while RX1 shows increase in all seasons.