Precipitation is an essential aspect of climate, and also drives many climate impacts. The primary tool for projecting future precipitation is climate models. Climate models are already being used, both directly and indirectly, to quantify anticipated impacts of climate for the purpose of making decisions. Improving precipitation in models requires (1) quantifying characteristics of precipitation in relevant observational datasets, (2) comprehensive comparison of climate model precipitation against observations, and (3) sustained model development efforts focus on improving precipitation in models. It also requires addressing the many characteristics of precipitation, ranging from its mean spatial pattern through its variability across timescales from hourly and diurnal extending through extreme events (whether dry or wet).

We invite presentations in this session that address:
- metrics to quantify the characteristics of precipitation in observations,
- evaluation of climate model simulations against observations, and
- development efforts aimed at improving precipitation in models (including seamless modeling systems).

Public information:
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Co-organized by AS1
Convener: Angeline PendergrassECSECS | Co-conveners: Margot badorECSECS, Jennifer Catto, Gill Martin, Christian Jakob
| Attendance Mon, 04 May, 10:45–12:30 (CEST)

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Chat time: Monday, 4 May 2020, 10:45–12:30

D3754 |
| solicited
Robin Chadwick, Peter Good, Christopher Holloway, John Kennedy, Jason Lowe, Romain Roehrig, and Stephanie Rushley

Seasonal mean tropical precipitation at any location is controlled by a tangle of local and remote effects, including influences from SSTs across the globe. This, along with uncertainty in precipitation observations, and extremely limited observations of atmospheric circulation, makes understanding the relevant physics challenging. Climate model precipitation biases persisting across multiple generations of models point towards stubborn gaps in understanding and reduce confidence in seasonal forecasts and climate projections.  This includes the 'double ITCZ problem': excessive rainfall in the southern tropical Pacific, first reported in 1995.  Model ITCZs also tend to be too wide.

Our study shows that in the real world, the sensitivity of tropical precipitation to local sea surface temperature is high, associated with strong shallow circulations.  This rests on a novel analysis of observations, unpicking local and remote controls on precipitation, and navigating a path through observational uncertainty.  Models with appropriate sensitivity to local sea surface temperature, perform well across many conditions.  Improvements in this sensitivity from the fifth to the sixth model intercomparison project are small, highlighting the need for new understanding.  By further linking model biases to shallow convection, our results highlight a target process for focused research: accelerating improvements in seasonal forecasts through to multi-decadal climate projections.

Wider Met Office work linking precipitation evaluation between climate, seasonal and weather timescales will also be summarised.

How to cite: Chadwick, R., Good, P., Holloway, C., Kennedy, J., Lowe, J., Roehrig, R., and Rushley, S.: High sensitivity of seasonal tropical precipitation to local sea-surface temperature, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7708, https://doi.org/10.5194/egusphere-egu2020-7708, 2020.

D3755 |
| Highlight
Stephanie Fiedler, Traute Crueger, Roberta D’Agostino, Karsten Peters, Tobias Becker, David Leutwyler, and Laura Paccini and the project team

Climate models are known to have biases in tropical precipitation. We assessed to what extent simulations of tropical precipitation have improved in the new Coupled Model Intercomparison Project (CMIP) phase six, using state-of-the-art observational products and model results from the earlier CMIP phases three and five. We characterize tropical precipitation with different well-established metrics. Our assessment includes (1) general aspects of the mean climatology like precipitation associated with the Intertropical Convergence Zone and shallow cloud regimes in the tropics, (2) solar radiative effects including the summer monsoons and the time of occurrence of tropical precipitation in the course of the day, (3) modes of internal variability such as the Madden-Julian Oscillation and the El Niño Southern Oscillation, and (4) changes in the course of the 20th century. The results point to improvements of CMIP6 models for some metrics, e.g., the occurrence of drizzle events and consecutive dry days. However, no improvements of CMIP6 models are identified for other aspects of tropical precipitation. These include the area and intensity of the global summer monsoon as well as the diurnal cycle of the tropical precipitation amount, frequency and intensity.

All our metrics taken together, CMIP6 models show no systematic improvement of tropical precipitation across different temporal and spatial scales. The model biases in the spatial distribution of tropical precipitation are typically larger than the changes associated with anthropogenic warming. Given the pace of climate change as compared to the pace of climate model improvements, we suggest to use novel modeling approaches to understand the responseof tropical precipitation to changes in atmospheric composition.

How to cite: Fiedler, S., Crueger, T., D’Agostino, R., Peters, K., Becker, T., Leutwyler, D., and Paccini, L. and the project team: Did tropical precipitation improve in CMIP6 simulations?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3579, https://doi.org/10.5194/egusphere-egu2020-3579, 2020.

D3756 |
George Tselioudis and Jasmine Remillard

In order to understand the mechanisms determining precipitation variability and to evaluate model skill in simulating those mechanisms, it is important to partition the precipitation field into regimes that include distinct sets of processes. In the past, dynamic fields like omega and SLP have been used to define regimes and study cloud, radiation, and precipitation variability. More recently, cloud-defined weather states were derived and used for similar analyses. Here, we apply a new cloud-defined Weather State dataset derived from the higher-resolution ISCCP-H data to examine precipitation variability at global scales and evaluate CMIP6 model precipitation simulations . In addition, precipitation partitioning using mid-tropospheric vertical velocity is performed, and the differences between the results of the two compositing methodologies are discussed.

How to cite: Tselioudis, G. and Remillard, J.: Evaluation of CMIP6 Model Precipitation Variability Through Compositing in Cloud-defined Weather States, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19865, https://doi.org/10.5194/egusphere-egu2020-19865, 2020.

D3757 |
Shuyi Chen and Brandon Kerns

Precipitation is a highly complex, multiscale entity in the global weather and climate system. It is affected by both global and local circulations over a wide range of time scales from hours to weeks and beyond. It is also an important measure of the water and energy cycle in climate models. To better understand the physical processes controlling precipitation in climate models, we need to evaluate precipitation not only in in terms of its global climatological distribution but also multiscale variability in time and space.

This study presents a new set of metrics to quantify characteristics of global precipitation using 20-years the TRMM-GPM Multisatellite Precipitation Analysis (TMPA) data from June 1998 to May 2018 over the global tropics-midlatitudes (50°S – 50°N) with 3-hourly and 0.25-degree resolutions.  We developed a method to identify large-scale precipitation objects (LPOs) using a temporal-spatial filter and then track the LPOs in time, namely the Large-scale Precipitation Tracking systems (LPTs) as described in Kerns and Chen (2016, 2020, JGR-Atmos). The most unique feature of this method is that it can distinguish large-scale precipitation organized by, for example, monsoons and the Madden-Julian Oscillation (MJO), from that of mesoscale and synoptic scale weather systems, as well as those relatively stationary local topographically and diurnally forced precipitation. The new precipitation metrics based on the satellite observation are used to evaluate climate models.  Early results show that most models overproduce precipitation over land in non-LPTs and underestimate large-scale precipitation (LPTs) over the oceans compared with the observations. For example, the MJO contributes up to 40-50% of the observed annual precipitation over the Indio-Pacific warm pool region, which are usually much less in the models because of models’ inability to represent the MJO dynamics. Furthermore, the spatial variability of precipitation associated with ENSO is more pronounced in the observations than models.

How to cite: Chen, S. and Kerns, B.: New Metrics to Quantify Spatial and Temporal Characteristics of Precipitation Using 20-years TRMM-GPM Data for Evaluating Climate Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21363, https://doi.org/10.5194/egusphere-egu2020-21363, 2020.

D3758 |
Mark Muetzelfeldt, Reinhard Schiemann, Andrew Turner, Nicholas Klingaman, and Pier Luigi Vidale

Climate models have a long-standing bias in the diurnal cycle of precipitation over land - they produce peak rainfall at local midday, when insolation is at its maximum. As part of the COnvective Scale Modelling In China (COSMIC) project, we investigate this bias over China using high-resolution (13 km) global simulations with the HadGEM3 model. We compare the diurnal cycle of summer precipitation with satellite observations of precipitation from CMORPH. The simulations are run with and without a convection parametrization scheme, as this scheme has been shown to be important for controlling the timing of precipitation. We analyse the amount, frequency and intensity of the precipitation, investigating their diurnal cycle and spatial distribution.

The analysis is performed on a grid-point scale, as well as at larger scales based on the catchment basins across the region. Catchment basins provide a natural way of linking the meteorological precipitation data to the underlying physical geography of the region, in a way which is useful for decision makers and could be used to provide information to hydrological models in the future. We present a simple Python tool for performing the analysis: BAsin-Scale Model Assessment ToolkIt (BASMATI).

In line with previous studies, we find that the simulation performed with parametrized convection produces precipitation over land which peaks too early in the day. The simulation performed with explicit convection generally produces peaks in precipitation which occur later in the day - closer in time to the observed peak. By comparing our results with published work, we find that the presence or absence of a convection parametrization scheme is more important for determining the spatial distribution of the time of peak precipitation than the resolution of the simulations. We present comparisons of precipitation in the simulations and observations performed at grid points and over catchment basins using BASMATI. The catchment basins are chosen based on their size, which allows for the comparison to be done as a function of spatial scale.

How to cite: Muetzelfeldt, M., Schiemann, R., Turner, A., Klingaman, N., and Vidale, P. L.: Comparing the diurnal cycle of precipitation in models and observations at different spatial scales over China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-7846, https://doi.org/10.5194/egusphere-egu2020-7846, 2020.

D3759 |
| Highlight
Michael Wehner, Peter gleckler, and Jiwoo Lee

Using a non-stationary Generalized Extreme Value statistical method, we calculate selected extreme daily precipitation indices and their 20 year return values from the CMIP5 and CMIP6 climate models over the historical and future periods. We evaluate model performance of these indices and their return values in replicating similar quantities calculated from multiple gridded observational products. Difficulties in interpreting model quality in the context of observational uncertainties are discussed. Projections are framed in terms of specified global warming target temperatures rather than at specific times and under specific emissions scenarios. The change in framing shifts projection uncertainty due to differences in model climate sensitivity from the values of the projections to the timing of the global warming target. At their standard resolutions, we find there are no meaningful differences between the two generations of models in their quality or projections of simulated extreme daily precipitation.

How to cite: Wehner, M., gleckler, P., and Lee, J.: Evaluation and projection of long period return values of extreme daily precipitation in the CMIP5 and CMIP6 models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3782, https://doi.org/10.5194/egusphere-egu2020-3782, 2020.

D3760 |
Hayley Fowler, Liz Lewis, Stephen Blenkinsop, David Pritchard, Selma Guerreiro, Roberto Villalobos Herrera, and Andreas Becker

Extremes of precipitation can cause flooding and droughts which can lead to substantial damages to infrastructure and ecosystems and can result in loss of life. It is still uncertain how hydrological extremes will change with global warming as we do not fully understand the processes that cause extreme precipitation under current climate variability. Progress has been limited so far in this area due to the lack of data available to researchers. The INTENSE project, part of the with the World Climate Research Programme (WCRP)'s Grand Challenge on 'Understanding and Predicting Weather and Climate Extremes', has used a novel and fully-integrated data-modelling approach to provide a step-change in our understanding of the nature and drivers of global sub-daily precipitation extremes and change on societally relevant timescales.

The first step towards achieving this was to construct a new global sub-daily precipitation dataset. The dataset contains hourly rainfall data from ~25,000 gauges across >200 territories from a wide range of sources. A rigorous, flexible quality-control algorithm has been developed to ensure that the data collected is as accurate as possible. The QC methodology combines a number of checks against neighbouring gauges, known biases and errors, and thresholds based on the Expert Team on Climate Change Detection and Indices (ETCCDI) Climate Change Indices.  An open source version of the QC software will set a new standard for verifying sub-daily precipitation data.

A set of global sub-daily precipitation indices have also been produced (and will be made freely available later this year) based upon stakeholder recommendations including indices that describe maximum rainfall totals and timing, the intensity, duration and frequency of storms, frequency of storms above specific thresholds and information about the diurnal cycle. The talk will discuss the major findings from the production of these new global sub-daily precipitation indices.

How to cite: Fowler, H., Lewis, L., Blenkinsop, S., Pritchard, D., Guerreiro, S., Villalobos Herrera, R., and Becker, A.: A Quality-Controlled Global Sub-daily Precipitation Dataset and Sub-daily Precipitation Indices, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20501, https://doi.org/10.5194/egusphere-egu2020-20501, 2020.

D3761 |
Jennifer Catto and Matthew Priestley

Process-based evaluation of precipitation is key to understanding climate model biases. It is vital to ensure that precipitation is produced in the model due to the correct mechanisms (or weather system). Atmospheric fronts have been shown to be responsible for a large proportion of total and extreme precipitation in the mid-latitudes. Therefore, representation of precipitation associated with fronts in climate models needs to be tested.

We applied objective front identification to the historical simulations from the CMIP6 archive and linked them with their 6-hourly precipitation accumulations. We compared the model outputs to the results from observationally constrained datasets. The fronts were identified from ERA5 and linked to precipitation estimates from sources including ERA5, and satellite products. This allows the precipitation errors to be decomposed into components associated with the frequency and intensity of frontal and non-frontal precipitation.

The diagnostics from the analysis have been made into metrics which could be used to evaluate model performance and aid in focussing future model development.

How to cite: Catto, J. and Priestley, M.: Evaluation of frontal precipitation in CMIP6 models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9371, https://doi.org/10.5194/egusphere-egu2020-9371, 2020.

D3762 |
Tímea Kalmár, Ildikó Pieczka, and Rita Pongrácz

Precipitation is one of the most important climate variables in many aspects due to its key impact on agriculture, water management, etc. However, it remains a challenge for climate models to realistically simulate the regional patterns, temporal variations, and intensity of precipitation. The difficulty arises from the complexity of precipitation processes within the atmosphere stemming from cloud microphysics, cumulus convection, large-scale circulations, planetary boundary layer (PBL) processes, and many others. This is especially true for heterogeneous surfaces with complex orography such as the Carpathian region.  Thus, the Carpathian Basin, with its surrounding mountains, requires higher model resolution, along with different parameterizations, compared to more homogenous regions. The aim of the study is to reproduce the historical precipitation pattern through testing the parameterization of surface processes. The appropriate representations of land surface component in climate models are essential for the simulation of surface and subsurface runoff, soil moisture, and evapotranspiration. Furthermore, PBL strongly influences temperature, moisture, and wind through the turbulent transfer of air mass. The current study focuses on the newest model version of RegCM (RegCM4.7), with which we carry out simulations using different parameterization schemes over the Carpathian region. We investigate the effects of land-surface schemes (i.e. BATS - Biosphere-Atmosphere Transfer Scheme and CLM4.5 - Community Land Model version 4.5) in the regional climate model. Studies over different regions have shown that CLM offers improvements in terms of land–atmosphere exchanges of moisture and energy and associated surface climate feedbacks compared with BATS. Our aim includes evaluating whether this is the case for the Carpathian region.

Four 1-year-long experiments both for 1981 and 2010 (excluding the spin-up time) are completed using the same domain, initial and lateral atmospheric boundary data conditions (i.e. ERA-Interim), with a 10 km spatial resolution. These years were chosen because 1981 was a normal year in terms of precipitation, while 2010 was the wettest year in Hungary from the beginning of the 20th century. We carry out a detailed analysis of RegCM outputs focusing not only on standard climatological variables (precipitation and temperature), but also on additional meteorological variables, which have important roles in the water cycle (e.g. soil moisture, evapotranspiration). The simulations are compared with the CARPATCLIM observed, homogenised, gridded dataset and other databases (ESA CCI Soil Moisture Product New Version Release (v04.5) and Surface Solar Radiation Data Set - Heliosat (SARAH)). It is found that the simulated near-surface temperature and precipitation are better represented in the CLM scheme than in the BATS when compared with observations, both over the lowland and mountainous area. The model simulations also show that the precipitation is overestimated more over mountainous area in 2010 than in 1981.  

How to cite: Kalmár, T., Pieczka, I., and Pongrácz, R.: Evaluation of the regional climate model RegCM4.7 over the Carpathian region for very wet and average years, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-330, https://doi.org/10.5194/egusphere-egu2020-330, 2020.

D3763 |
Maria Fernanda R. Pereima, Pablo B. Amorim, Tassia M. Brighenti, Regina R. Rodrigues, and Pedro Luiz B. Chaffe

Southern Brazil is in a transitional zone between tropical and extratropical climates. The rainfall regime in such transitional zones can be rather sensitive to climate change and related expansion of the tropics in the Southern Hemisphere. It is expected that rainfall will increase up to 30% over this area in the next decades. It is important, however, to investigate if the mechanisms that generate rainfall are simulated correctly in the models to know when downscaling and bias correction methods should be applied. The objective of this study is to evaluate the performance of the CMIP5 climate models in terms of precipitation in southern Brazil. This study addresses fundamental aspects of model evaluation and aims to give guidance on the proper use of climate model outputs for southern Brazil. We use 41 historical climate simulations and 22 RCP8.5 future climate simulations for the periods of 1980-2005 and 2070-2100, respectively. We compare the historical simulations with an interpolated product database obtained from ground stations. To evaluate the model performance regarding its marginal distribution, we use the following metrics: annual rainfall, variance, skewness, dry day fraction, wet day fraction, high percentiles and similarity of distributions (trough Kolmogorov-Smirnov test). There is a negative bias in all of them except for wet day fraction. All metrics of temporal aspects such as Markham’s seasonality index, autocorrelation, time of the annual maxima, dry spell average and maximum lengths, wet spell average and maximum lengths show a positive bias, apart from the time of annual maxima. Overall, annual rainfall is expected to increase in southern Brazil. Spatial patterns of annual rainfall are similar in the RCP 8.5 future pathways to the ones found in the historical period, with wetter areas expanding toward the north. However, the spatial pattern of observed rainfall is not captured by climate models. They simulate smaller volumes of precipitation in the southern border. A similar pattern was found in extreme precipitation, with bias almost twice as large than the one found in annual rainfall. Furthermore, the models do not properly represent the seasonal cycle, the Markham’s seasonality index reached four times the observed in some areas. Given the poor performance in the area, the use of future simulations in impact studies should be done carefully once the direct use of climate model precipitation in hydrological studies could result in misleading conclusions.

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001

How to cite: Pereima, M. F. R., Amorim, P. B., Brighenti, T. M., Rodrigues, R. R., and Chaffe, P. L. B.: How well does climate model perform for southern Brazil? , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11114, https://doi.org/10.5194/egusphere-egu2020-11114, 2020.

D3764 |
Maria Chara Karypidou and Eleni Katragkou

One of the main features controlling precipitation over southern Africa during the wet season is the Angola Low (AL) pressure system that appears as a heat low during October and November and as a tropical low during the climatological mean of December, January and February. The literature provides evidence that wet biases over southern Africa in the Coupled Model Intercomparison Project Phase 5 ensemble (CMIP5) are associated with a strongly simulated AL. In the current work, we examine the degree to which this observation holds for the CORDEX-Africa (Coordinated Regional Climate Downscaling Experiment - Africa) ensemble, using evaluation experiments forced with ERA-Interim at a spatial resolution of 0.44o. The analysis is performed using daily values for months October to March for the period 1990-2008. We characterize the precipitation bias over southern Africa using 10 satellite and gridded precipitation products. For the identification of the AL we use potential temperature at 850 hPa, specific humidity at 850 hPa and relative vorticity at 850 and 500 hPa. Our results highlight the fact that process-based evaluation of climate simulations are key in understanding structural model deficiencies. 

How to cite: Karypidou, M. C. and Katragkou, E.: Precipitation biases over southern Africa: examining the role of the Angola Low., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1177, https://doi.org/10.5194/egusphere-egu2020-1177, 2020.

D3765 |
Zhen Liu, Massimo A. Bollasina, Laura J. Wilcox, José M. Rodríguez, and Leighton A. Regayre

Monsoon biases are long-standing and an important problem to solve because nearly half of the world’s population is affected by monsoon precipitation and circulation. The effect of local and remote circulation biases on Asian monsoon biases is studied with dynamical nudging using the latest version of the atmospheric component of the HadGEM3 model. Constraining the large-scale circulation substantially reduces oceanic biases in precipitation and circulation, particularly over the extra-tropics. Tropical wet biases may become even stronger because of unconstrained convection. By contrast, model biases over land are less sensitive to nudging due to the prominent role of local planetary boundary layer processes in modulating the low-level circulation. Nudging reduces the seasonal excess (deficit) precipitation over India in winter (summer) by reducing the local cyclonic (anti-cyclonic) biases. Constraining the circulation outside Asia demonstrates that the wet (dry) biases are mostly remotely (locally) controlled in winter (summer) over India. The monsoon biases over China show small changes with nudging, suggesting they are more thermodynamically driven. Monsoon variability is improved over India but not over China in nudged simulations. Despite the remaining errors in nudged simulations, our study suggests that nudging serves as a useful tool to disentangle the contribution of regional and remote circulation in generating the monsoon responses.

How to cite: Liu, Z., Bollasina, M. A., Wilcox, L. J., Rodríguez, J. M., and Regayre, L. A.: Contrasting the role of regional and remote circulation in driving the Asian monsoon in HadGEM3-GA7 , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2401, https://doi.org/10.5194/egusphere-egu2020-2401, 2020.

D3766 |
Harry Mutton, Mat Collins, Hugo Lambert, and Rob Chadwick

The Monsoons produce some of the largest levels of uncertainty in projected precipitation change across the globe, and addressing this uncertainty is a key issue that must be faced in order to allow correct adaptation policy to be put in place.


A set of CMIP6 GCM experiments, that allow the full effect of CO2 forcing to be decomposed into individual components, highlight the leading factors that produce changes in monsoon precipitation. The results reveal a high spatial variability in these factors, with changes in the Indian Monsoon dominated by the effect of sea surface temperatures and the direct radiative effect of increased CO2, and changes in the South American Monsoon governed by the plant physiological effect and the direct radiative effect of increased CO2. The processes behind these precipitation changes are also investigated by looking at variations in atmospheric circulation and surface temperature. Results of the patterned sea surface temperature experiment demonstrate a slow-down of the Indian Monsoon circulation possibly driven by an anomalously warm Indian Ocean.


This analysis has been performed for all land monsoon regions, decomposing the full CO2 forcing into; uniform and patterned sea surface temperature change, the plant physiological effect, the direct radiative effect and the impact of sea-ice melt. These results can help identify emergent constraints, as well as indicate which aspects of climate models need to be improved in order to reduce model uncertainty.

How to cite: Mutton, H., Collins, M., Lambert, H., and Chadwick, R.: Investigating the factors affecting Monsoon precipitation under climate change, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4942, https://doi.org/10.5194/egusphere-egu2020-4942, 2020.

D3767 |
Martin Gomez-Garcia, Akiko Matsumura, and Daikichi Ogawada

The post-processing of the Earth System Models (ESMs) outputs has become a routine step that is taken in climate change impact assessments with the aim of (i) reproducing the probability distribution of the corresponding observed data and (ii) correcting the biases in the probability distributions of projected future climate. To responsibly support the decision‐making processes, the climate‐modeling community has been discussing about the conceptual requirements that bias‐correction methods should fulfill to avoid altering the relevant information that is provided by ESMs, like the climate trends or the inter-variable physical dependence structure. Bearing in mind these discussions, a recently proposed method of bias-correction, based on TRend-preserving Synthetic Samples of Stable Distributions (TR3S), decomposes the atmospheric variables into three temporal elements that represent the climate mean state, the interannual variability, and the daily variability. This decomposition is aimed at correcting the biases at one time scale without affecting the projected climate trend or the distributional properties at other time scales. The novelty of this approach is, nevertheless, marked by the adjustment of interannual and daily variability that is made by replacing the ESM‐simulated variability with synthetic samples drawn from Stable Distributions (SDs) that were previously fitted to the observed variability. The replacement prevents the transfer of the sampling variability of the calibration period while giving the corrected data the distributional properties of the observed climate. The employment of SDs was motivated by the fact that the ESM-projected changes in the scale, the symmetry, and the frequency of extremes can be measured and applied to the SDs of the observed data. In this work, we correct the biases in the global precipitation datasets generated by several ESMs using the TR3S method and present the projected changes of a few indices of extremes using online interactive maps. Furthermore, the TR3S method allowed us to document the spatial distribution of the biases in the distributional properties (i.e., scale, symmetry, and frequency of extremes) of daily and interannual variability of each ESM. We hope that the bias-corrected information can be useful to end-users in impact assessments and the analytical framework of model biases can be used by modelers to identify ways in which the ESM parameterizations could be improved.

How to cite: Gomez-Garcia, M., Matsumura, A., and Ogawada, D.: ESM-projected global change in indices of extreme precipitation using the TR3S method of bias-correction, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15523, https://doi.org/10.5194/egusphere-egu2020-15523, 2020.

D3768 |
Andrew Williams and Paul O'Gorman

Changes in extreme precipitation are amongst the most impactful consequences of global warming, with potential effects ranging from increased flood risk and landslides to crop failures and impacts on ecosystems. Thus, understanding historical and future changes in extreme precipitation is not only important from a scientific perspective, but also has direct societal relevance.

However, while most current research has focused on annual precipitation extremes and their response to warming, it has recently been noted that climate model projections show a distinct seasonality to future changes in extreme precipitation. In particular, CMIP5 models suggest that over Northern Hemisphere (NH) land the summer response is weaker than the winter response in terms of percentage changes.

Here we investigate changes in seasonal precipitation extremes using observations and simulations with coupled climate models. First, we analyse observed trends from the Hadley Centre’s global climate extremes dataset (HadEX2) to investigate to what extent there is already a difference between summer and winter trends over NH land. Second, we use 40 ensemble members from the CESM Large Ensemble to characterize the role played by internal variability in trends over the historical period. Lastly, we use CMIP5 simulations to explore the possibility of a link between the seasonality of changes in precipitation extremes and decreases in surface relative humidity over land.

How to cite: Williams, A. and O'Gorman, P.: Investigating the seasonal response of precipitation extremes to global warming using observations and large-ensembles of coupled climate models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5989, https://doi.org/10.5194/egusphere-egu2020-5989, 2020.

D3769 |
Daniel Argüeso, Alejandro Di Luca, Nicolas Jourdain, Romualdo Romero, and Victor Homar

The Maritime Continent is a major convective area and precipitation processes in the region pose great challenges to atmospheric models. A combination of large-scale drivers, such as the Madden-Julian Oscillation and ENSO, and fine-scale processes, such as orographically-forced precipitation, land-sea circulations and tropical convection, governs rainfall in the Maritime Continent. The use of convection-permitting models in the region has shown improved performance in the simulation of precipitation characteristics that are key for the region (i.e. diurnal cycle).

Most of the rainfall occurring over land is concentrated in the late afternoon and precipitation extremes often occur over short periods of time. The availability of water vapor in the lower troposphere and the high water-holding capacity of a warm atmosphere favors very intense precipitation events, according to the Clausius-Clapeyron relationship. In a warming climate, a full understanding of the so-called precipitation scaling with temperature is thus crucial. However, this potential generally requires the atmosphere be saturated and convection be initiated to become effective. Using a regional climate model operating at convection-permitting scales over 3 consecutive wet seasons, we investigate the response of intense precipitation to temperature.

In this presentation, we examine different approaches to relate precipitation extremes to near-surface temperature and dew-point temperature. We show that the relationship breaks at certain thresholds that are relatively uniform across islands. The region is well supplied with water vapor and the break is not explained by a deficit in water vapor, unlike previously proposed for other water-limited regions. We identify possible reasons for this behavior, such as the lack of environmental conditions that trigger convection. In this context, we explore the sensitivity of the modelling system to the convection representation (explicit vs. parameterized) and discuss the implications for future changes in intense precipitation events. Finally, we put forward the use of specific variables, such as temperature and equivalent potential temperature integrated in the vertical. These variables not only are coherent with the CC equation but also acknowledge the different warming rates near the surface and at higher tropospheric levels, where precipitating processes actually occur.

How to cite: Argüeso, D., Di Luca, A., Jourdain, N., Romero, R., and Homar, V.: Precipitation scaling in the tropics with a convection-permitting model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6890, https://doi.org/10.5194/egusphere-egu2020-6890, 2020.

D3770 |
Yuting Chen, Athanasios Paschalis, and Christian Onof

Sub-daily precipitation at fine temporal resolution (~1 km2) is critical for a wide range of hydrological applications, such as flooding estimation, urban drainage design. In recent years, a step-change was given by Km-scale Convection-permitting models (CPMs), allowing for the first-time climate change projections at hydrologically relevant scales. CPMs have been now introduced in the operational climate change projections of the Met Office in the UK (UCKP18). The high-resolution hourly precipitation at a 2.2 km scales is currently available for the historical period (1980-2000) and future period (2020-2080) for the RCP8.5 scenario. It is perceived to provide a plausible tool for detailed climate impact studies. However, a question remains unanswered: is the local projection of precipitation from UKCP18 credible for hydrological use? 

To answer the question, simulated hourly precipitation from the UKCP18 for the historical period is compared statistically with the observed rainfall data. Observation rainfall was obtained from UK Met Office C-band Weather Radar network and Gridded estimates of daily areal rainfall (CEH-GEAR). These were used to assess the spatial-temporal structure of rainfall, including spatial spectra, distributions of rainfall cell sizes and intensities, and their temporal growth/decay dynamics, and rainfall extremes. The statistical evaluation was performed for all climatologically distinct regions of the UK on a seasonal basis.

The results show that hourly precipitation in UKCP18 has a realistic spatial correlation structure compared to observations. However, the extreme areal mean precipitation is overestimated, particularly at scales finer than 6.6 km. Significant differences between the size and temporal dynamics of observed and modelled rainfall cells were identified, with distinct differences amongst climate regimes, highlighting the limits of applicability of current generation CPMs for hydrological forecasting.

How to cite: Chen, Y., Paschalis, A., and Onof, C.: Evaluating the small-scale space time structure of rainfall in the Convection Permitting Model of UKCP18, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8007, https://doi.org/10.5194/egusphere-egu2020-8007, 2020.

D3771 |
Samar Minallah and Allison Steiner

The Laurentian Great Lakes region has a distinct precipitation seasonality, with highest magnitudes in the summer months of June to September and drier conditions in the winter months (December to March). The region also exhibits a ‘mid-summer drying’ behaviour, where the precipitation magnitude drops from July to August by approximately 7% and recovers in September before declining again in the autumn season. The distinct precipitation seasonal cycle modulates the land hydrological budget and has significance for regional water resources. This study aims to understand the precipitation seasonality in 20 CMIP6 models for historical (1980 – 2014) and mid-century (2030 – 2060) SSP2-4.5 scenario. Seasonal wet/dry biases in historical data are computed using CRU TS4.03 precipitation data as baseline.

CMIP6 models show a myriad of different patterns, none of which conform to the observed precipitation seasonality. Some models show a singular skewed peak with the maxima in either June or July flowed by slow tapering off until December (e.g., MRI-ESM2.0, CanESM, GFDL-CM4). Various models show a spring and winter-time wet bias (NUIST-NESM3, ACCESS-ESM1-5) and/or underestimation of the summer-season magnitudes (FGOALS-f3-L, NCAR-CESM2, NorESM2-MM). In general, the precipitation seasonality exhibited by the CMIP6 models is not characteristic of the region. We also find that while some models are wet or dry throughout the year, others show only seasonal biases indicating that their convective parameterization and/or microphysics schemes fail to adequality capture precipitation patterns in these seasons. While most CMIP6 models and reanalysis datasets show a gaussian convective precipitation cycle with the annual maxima in July, some models (e.g., BCC-CSM2-MR, NCAR-CESM2) show strong biases in it, indicating issues with their convective schemes.

These biases and anomalous precipitation cycle can be propagated or even amplified in the future climate model simulations, significantly altering the projections. Therefore, identifying the models that best represent the regional precipitation spatiotemporal characteristics can assist in better assessment of the future changes in the region’s hydroclimate.

How to cite: Minallah, S. and Steiner, A.: Evaluating the precipitation regime of the Great Lakes Region in CMIP6 models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12291, https://doi.org/10.5194/egusphere-egu2020-12291, 2020.

D3772 |
Ralph Trancoso and Jozef Syktus

Changing precipitation patterns due to climate change is a critical concern affecting society and the environment. Projected changes in global seasonal precipitation are largely heterogeneous in space, time, magnitude and direction. Therefore, reconciling projected future precipitation is pivotal for climate change science and adaptation and mitigation schemes.

This research contributes to disentangle future precipitation uncertainty globally by exploring long-term trends in projected seasonal precipitation of 33 CMIP5 and 16 CMIP6 models for the period 1980-2100. We first estimate trend slopes and significance in long-term future seasonal precipitation using the Sen-Slope and Mann-Kendall tests and constrain trends with at least 10% of cumulative changes over the 120-year period. Then, we assess convergence in the direction of trends across seasons. We highlight the world’s jurisdictions with consistent drying and wetting patterns as well as the seasonal dominance of precipitation trends.

A consistent drying pattern – where at least 78% of GCMs have decreasing precipitation trends – was observed in Central America, South and North Africa, South Europe, Southern USA and Southern South America. Unlike, a strong convergence in projected long-term wetness – where at least 78% of GCMs have increasing precipitation trends – was observed across most of Asia, Central Africa, Northern Europe, Canada, Northern US and South Brazil and surrounds.

Results show convergence in direction of seasonal precipitation trends revealing the world’s jurisdictions more likely to experience changes in future precipitation patterns. The approach is promisor to summarize trends in seasonal time-series from multiple GCMs and better constrain wetting and drying precipitation patterns. This study provides meaningful insights to inform water resource management and climate change adaptation globally.

How to cite: Trancoso, R. and Syktus, J.: Reconciling global projections of precipitation with CMIP6 and CMIP5 multi-model trends, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13321, https://doi.org/10.5194/egusphere-egu2020-13321, 2020.

D3773 |
Ping Huang

In the state-of-the-art CMIP5/6 models, there is an apparent excessive rainfall bias with a negative SST bias in the tropical Pacific intertropical convergence zone (ITCZ). The regime of the excessive ITCZ but negative SST bias is inconsistent with the common positive rainfall–SST correlation. Using a two-mode model, we decomposed the rainfall bias into two components, and found that the surface convergence (SC) bias is the key factor forming the excessive ITCZ bias in the historical runs of 25 CMIP5 models and 23 CMIP6 models. A mixed layer model was further applied to connect the formation of the SC bias with the SST pattern bias. The results suggest that the meridional pattern of the SST bias plays a key role in forming the SC bias. In the CMIP5/6 models, the overall negative SST bias has two apparent meridional troughs at around 10°S and 10°N, respectively. The two meridional troughs in the SST bias drive two convergence centers in the SC bias favoring the excessive ITCZ, even though the local SST bias is negative.

How to cite: Huang, P.: Excessive ITCZ but negative SST biases in the tropical Pacific simulated by CMIP5/6 models: The role of the meridional pattern of SST bias, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4421, https://doi.org/10.5194/egusphere-egu2020-4421, 2020.

D3774 |
Baijun Tian

The double-Intertropical Convergence Zone (ITCZ) bias is one of the most outstanding problems in climate models. This study seeks to examine the double-ITCZ bias in the latest state-of-the-art fully coupled global climate models that participated in Coupled Model Intercomparison Project (CMIP) Phase 6 (CMIP6) in comparison to their previous generations (CMIP3 and CMIP5 models). To that end, we have analyzed the long-term annual mean tropical precipitation distributions and several precipitation bias indices that quantify the double-ITCZ biases in 75 climate models including 24 CMIP3 models, 25 CMIP3 models, and 26 CMIP6 models. We find that the double-ITCZ bias and its big inter-model spread persist in CMIP6 models but the double-ITCZ bias is slightly reduced from CMIP3 or CMIP5 models to CMIP6 models.

How to cite: Tian, B.: The Double-ITCZ Bias in CMIP6 Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21375, https://doi.org/10.5194/egusphere-egu2020-21375, 2020.

D3775 |
Gill Martin, Nicholas Klingaman, Segolene Berthou, Rob Chadwick, Elizabeth Kendon, and Aurel Moise

The need for improved understanding of how a warming climate may change precipitation variability and extremes has focused model developers' attention on the inability of convection parameterizations to represent the observed range of deep convective processes. Under particular scrutiny are the consequences of poorly simulated sub-daily, grid-point precipitation variability on rainfall distributions at longer (e.g., daily, seasonal, decadal) timescales and larger spatial scales. Lack of knowledge or understanding of the spatial and temporal variability in rainfall, in observations and models, hampers model development and can undermine our confidence in projections. A major challenge in advancing our understanding is a lack of comprehensive diagnostics and metrics for analysing the characteristics of both observed and modelled preciptitation across time and space scales. 

The ASoP diagnostic package (Analysing Scales of Precipitation; Klingaman et al. 2017; Martin et al., 2017) has been developed and applied to various model and observation datasets over the past few years. ASoP can be applied to data ranging from the gridscale and time-step to regional and sub-monthly averages, and measures the spectrum of precipitation intensity, temporal variability as a function of intensity, and spatial and temporal coherence. When applied to time-step, gridscale tropical precipitation from a range of models, the diagnostics reveal that, far from the "dreary" persistent light rainfall implied by daily mean data, most models produce a broad range of time step intensities that span 1-100 mm/day. Averaging precipitation to a common spatial (km) or temporal (3h) resolution substantially reduces variability among models, demonstrating that averaging hides a wealth of information about intrinsic model behaviour. 

ASoP analysis of tropical rainfall variability in MetUM simulations at a range of horizontal resolutions shows that the behaviour of the deep convection parametrization in this model on the native grid and time step is largely independent of the grid-box size and time step length over which it operates. There is also little difference in the rainfall variability on larger/longer spatial/temporal scales. Tropical convection in the model on the native grid/time step is spatially and temporally intermittent, producing very large rainfall amounts interspersed with grid boxes/time steps of little or no rain. Spatial and temporal averaging smoothes out this intermittency such that, on the km scale, for oceanic regions, the spectra of 3-hourly and daily mean rainfall in the MetUM agree fairly well with those from satellite-derived rainfall estimates, while at 10-day timescales the averages are overestimated, indicating a lack of intra-seasonal variability. Over tropical land the results are more varied, but the model often underestimates the daily mean rainfall (partly as a result of a poor diurnal cycle) but still lacks variability on intra-seasonal timescales. ASoP diagnostics have also been applied to European rainfall (Berthou et al., 2018) and in high-resolution rainfall projections for the United Kingdom (Kendon et al., 2020). Such work is shedding light on how uncertainties in modelling small-/short-scale processes relate to uncertainty in climate change projections of rainfall distribution and variability, with a view to reducing such uncertainty through improved modelling of small-/short-scale processes.

How to cite: Martin, G., Klingaman, N., Berthou, S., Chadwick, R., Kendon, E., and Moise, A.: Understanding rainfall characteristics in climate models and observations, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21884, https://doi.org/10.5194/egusphere-egu2020-21884, 2020.

D3776 |
Peter Gleckler and Angeline Pendergrass

In this presentation we discuss a community-based effort to establish the benchmarking of simulated precipitation in Earth System Models.   We first summarize the impetus and outcomes of a recent workshop dedicated to the topic.    This includes the identification of a tiered system of objective tests (metrics) for the following climatological characteristics:  the mean state, seasonal cycle, variability across time scales, intensity/frequency distributions, extremes and drought.   Preliminary results are shown gauging model performance changes across multiple generations of CMIP.   The performance tests we describe are part of an open-source analysis framework being made available to model developers to help them make judgements about the quality of simulated precipitation during the model development process.

How to cite: Gleckler, P. and Pendergrass, A.: Using objective comparisons of observed and simulated precipitation to help guide the improvement of Earth System Models, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20472, https://doi.org/10.5194/egusphere-egu2020-20472, 2020.