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Bart van den Hurk
,
Janneke Ettema
, and
Pedro Viterbo

Abstract

This study aims at stimulating the development of soil moisture data assimilation systems in a direction where they can provide both the necessary control of slow drift in operational NWP applications and support the physical insight in the performance of the land surface component. It addresses four topics concerning the systematic nature of soil moisture data assimilation experiments over Europe during the growing season of 2000 involving the European Centre for Medium-Range Weather Forecasts (ECMWF) model infrastructure. In the first topic the effect of the (spinup related) bias in 40-yr ECMWF Re-Analysis (ERA-40) precipitation on the data assimilation is analyzed. From results averaged over 36 European locations, it appears that about half of the soil moisture increments in the 2000 growing season are attributable to the precipitation bias. A second topic considers a new soil moisture data assimilation system, demonstrated in a coupled single-column model (SCM) setup, where precipitation and radiation are derived from observations instead of from atmospheric model fields. For many of the considered locations in this new system, the accumulated soil moisture increments still exceed the interannual variability estimated from a multiyear offline land surface model run. A third topic examines the soil water budget in response to these systematic increments. For a number of Mediterranean locations the increments successfully increase the surface evaporation, as is expected from the fact that atmospheric moisture deficit information is the key driver of soil moisture adjustment. In many other locations, however, evaporation is constrained by the experimental SCM setup and is hardly affected by the data assimilation. Instead, a major portion of the increments eventually leave the soil as runoff. In the fourth topic observed evaporation is used to evaluate the impact of the data assimilation on the forecast quality. In most cases, the difference between the control and data assimilation runs is considerably smaller than the (positive) difference between any of the simulations and the observations.

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Bart J. J. M. van den Hurk
and
Erik van Meijgaard

Abstract

Land–atmosphere interaction at climatological time scales in a large area that includes the West African Sahel has been explicitly explored in a regional climate model (RegCM) simulation using a range of diagnostics. First, areas and seasons of strong land–atmosphere interaction were diagnosed from the requirement of a combined significant correlation between soil moisture, evaporation, and the recycling ratio. The northern edge of the West African monsoon area during June–August (JJA) and an area just north of the equator (Central African Republic) during March–May (MAM) were identified. Further analysis in these regions focused on the seasonal cycle of the lifting condensation level (LCL) and the convective triggering potential (CTP), and the sensitivity of CTP and near-surface dewpoint depressions HIlow to anomalous soil moisture. From these analyses, it is apparent that atmospheric mechanisms impose a strong constraint on the effect of soil moisture on the regional hydrological cycle.

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Siswanto
,
Geert Jan van Oldenborgh
,
Gerard van der Schrier
,
Geert Lenderink
, and
Bart van den Hurk
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Mxolisi E. Shongwe
,
Geert Jan van Oldenborgh
,
Bart van den Hurk
, and
Maarten van Aalst

Abstract

Probable changes in mean and extreme precipitation in East Africa are estimated from general circulation models (GCMs) prepared for the Intergovernmental Panel on Climate Change Fourth Assessment Report (AR4). Bayesian statistics are used to derive the relative weights assigned to each member in the multimodel ensemble. There is substantial evidence in support of a positive shift of the whole rainfall distribution in East Africa during the wet seasons. The models give indications for an increase in mean precipitation rates and intensity of high rainfall events but for less severe droughts. Upward precipitation trends are projected from early this (twenty first) century. As in the observations, a statistically significant link between sea surface temperature gradients in the tropical Indian Ocean and short rains (October–December) in East Africa is simulated in the GCMs. Furthermore, most models project a differential warming of the Indian Ocean during boreal autumn. This is favorable for an increase in the probability of positive Indian Ocean zonal mode events, which have been associated with anomalously strong short rains in East Africa. On top of the general increase in rainfall in the tropics due to thermodynamic effects, a change in the structure of the Eastern Hemisphere Walker circulation is consistent with an increase in East Africa precipitation relative to other regions within the same latitudinal belt. A notable feature of this change is a weakening of the climatological subsidence over eastern Kenya. East Africa is shown to be a region in which a coherent projection of future precipitation change can be made, supported by physical arguments. Although the rate of change is still uncertain, almost all results point to a wetter climate with more intense wet seasons and less severe droughts.

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Chiem van Straaten
,
Kirien Whan
,
Dim Coumou
,
Bart van den Hurk
, and
Maurice Schmeits

Abstract

Reliable subseasonal forecasts of high summer temperatures would be very valuable for society. Although state-of-the-art numerical weather prediction (NWP) models have become much better in representing the relevant sources of predictability like land and sea surface states, the subseasonal potential is not fully realized. Complexities arise because drivers depend on the state of other drivers and on interactions over multiple time scales. This study applies statistical modeling to ERA5 data, and explores how nine potential drivers, interacting on eight time scales, contribute to the subseasonal predictability of high summer temperatures in western and central Europe. Features and target temperatures are extracted with two variations of hierarchical clustering, and are fitted with a machine learning (ML) model based on random forests. Explainable AI methods show that the ML model agrees with physical understanding. Verification of the forecasts reveals that a large part of predictability comes from climate change, but that reliable and valuable subseasonal forecasts are possible in certain windows, like forecasting monthly warm anomalies with a lead time of 15 days. Contributions of each driver confirm that there is a transfer of predictability from the land and sea surface state to the atmosphere. The involved time scales depend on lead time and the forecast target. The explainable AI methods also reveal surprising driving features in sea surface temperature and 850 hPa temperature, and rank the contribution of snow cover above that of sea ice. Overall, this study demonstrates that complex statistical models, when made explainable, can complement research with NWP models, by diagnosing drivers that need further understanding and a correct numerical representation, for better future forecasts.

Open access
Chiem van Straaten
,
Kirien Whan
,
Dim Coumou
,
Bart van den Hurk
, and
Maurice Schmeits

Abstract

Subseasonal forecasts are challenging for numerical weather prediction (NWP) and machine learning models alike. Forecasting 2-m temperature (t2m) with a lead time of 2 or more weeks requires a forward model to integrate multiple complex interactions, like oceanic and land surface conditions leading to predictable weather patterns. NWP models represent these interactions imperfectly, meaning that in certain conditions, errors accumulate and model predictability deviates from real predictability, often for poorly understood reasons. To advance that understanding, this paper corrects conditional errors in NWP forecasts with an artificial neural network (ANN). The ANN postprocesses ECMWF extended-range summer temperature forecasts by learning to correct the ECMWF-predicted probability that monthly t2m in western and central Europe exceeds the climatological median. Predictors are objectively selected from ECMWF forecasts themselves, and from states at initialization, i.e., the ERA5 reanalysis. The latter allows the ANN to account for sources of predictability that are biased in the NWP model itself. We attribute ANN corrections with two explainable artificial intelligence (AI) tools. This reveals that certain erroneous forecasts relate to tropical western Pacific Ocean sea surface temperatures at initialization. We conjecture that the atmospheric teleconnection following this source of predictability is imperfectly represented by the ECMWF model. Correcting the associated conditional errors with the ANN improves forecast skill.

Significance Statement

We want to understand occasions in which a numerical weather prediction (NWP) model fails to forecast a predictable event existing in the real world. For forecasts of European summer weather more than 2 weeks in advance, real predictable events are rare. When misrepresented by the model, predicted future states become needlessly biased. We diagnose these missed opportunities with an explainable neural network. The neural network is aware of the initial state and learns to correct the NWP forecast on occasions when it misrepresents a teleconnection from the western tropical Pacific Ocean to Europe. The explainable architecture can be useful for other applications in which conditional model errors need to be understood and corrected.

Open access
Bart van den Hurk
,
Martin Best
,
Paul Dirmeyer
,
Andy Pitman
,
Jan Polcher
, and
Joe Santanello

No abstract available.

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Adriaan J. Teuling
,
Remko Uijlenhoet
,
Bart van den Hurk
, and
Sonia I. Seneviratne

Abstract

Integration of simulated and observed states through data assimilation as well as model evaluation requires a realistic representation of soil moisture in land surface models (LSMs). However, soil moisture in LSMs is sensitive to a range of uncertain input parameters, and intermodel differences in parameter values are often large. Here, the effect of soil parameters on soil moisture and evapotranspiration are investigated by using parameters from three different LSMs participating in the European Land Data Assimilation System (ELDAS) project. To prevent compensating effects from other than soil parameters, the effects are evaluated within a common framework of parsimonious stochastic soil moisture models. First, soil parameters are shown to affect soil moisture more strongly than the average evapotranspiration. In arid climates, the effect of soil parameters is on the variance rather than the mean, and the intermodel flux differences are smallest. Soil parameters from the ELDAS LSMs differ strongly, most notably in the available moisture content between the wilting point and the critical moisture content, which differ by a factor of 3. The ELDAS parameters can lead to differences in mean volumetric soil moisture as high as 0.10 and an average evapotranspiration of 10%–20% for the investigated parameter range. The parsimonious framework presented here can be used to investigate first-order parameter sensitivities under a range of climate conditions without using full LSM simulations. The results are consistent with many other studies using different LSMs under a more limited range of possible forcing conditions.

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Dirk Meetschen
,
Bart J. J. M. van den Hurk
,
Felix Ament
, and
Matthias Drusch

Abstract

High-quality fields of surface radiation fluxes are required for the development of Land Data Assimilation Systems. A fast offline integration scheme was developed to modify NWP model cloud fields based on Meteosat visible and infrared observations. From the updated cloud fields, downward shortwave and longwave radiation at the surface are computed using the NWP radiative transfer model.

A dataset of 15 months covering Europe was produced and validated against measurements of ground stations on a daily basis. In situ measurements are available for 30 stations in the Netherlands and two Baseline Surface Radiation Network (BSRN) stations in Germany and France. The accuracy of shortwave surface radiation is increased when the integration system is applied. The rms error in the model forecast is found to be 32 and 42 W m−2 for the period from October 1999 to December 2000 for the two BSRN stations. These values are reduced to 21 and 25 W m−2 through the application of the integration scheme. During the summer months the errors are generally larger than in winter. Because of an integrated monitoring of surface albedo, the performance of the scheme is not affected by snow cover. The errors in the longwave radiation field of the original NWP model are already small. However, they are slightly reduced by applying the integration scheme.

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Bart van den Hurk
,
Martin Hirschi
,
Christoph Schär
,
Geert Lenderink
,
Erik van Meijgaard
,
Aad van Ulden
,
Burkhardt Rockel
,
Stefan Hagemann
,
Phil Graham
,
Erik Kjellström
, and
Richard Jones

Abstract

Simulations with seven regional climate models driven by a common control climate simulation of a GCM carried out for Europe in the context of the (European Union) EU-funded Prediction of Regional scenarios and Uncertainties for Defining European Climate change risks and Effects (PRUDENCE) project were analyzed with respect to land surface hydrology in the Rhine basin. In particular, the annual cycle of the terrestrial water storage was compared to analyses based on the 40-yr ECMWF Re-Analysis (ERA-40) atmospheric convergence and observed Rhine discharge data. In addition, an analysis was made of the partitioning of convergence anomalies over anomalies in runoff and storage. This analysis revealed that most models underestimate the size of the water storage and consequently overestimated the response of runoff to anomalies in net convergence. The partitioning of these anomalies over runoff and storage was indicative for the response of the simulated runoff to a projected climate change consistent with the greenhouse gas A2 Synthesis Report on Emission Scenarios (SRES). In particular, the annual cycle of runoff is affected largely by the terrestrial storage reservoir. Larger storage capacity leads to smaller changes in both wintertime and summertime monthly mean runoff. The sustained summertime evaporation resulting from larger storage reservoirs may have a noticeable impact on the summertime surface temperature projections.

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