Soil moisture effects on seasonal temperature and precipitation forecast scores in Europe
The Second Global Land Atmosphere Coupling Experiment (GLACE2) is designed to explore the improvement of forecast skill of summertime temperature and precipitation up to 8 weeks ahead by using realistic soil moisture initialization. For the European continent, we show in this study that for temperature the skill does indeed increase up to 6 weeks, but areas with (statistically significant) lower skill also exist at longer lead times. The skill improvement is smaller than shown earlier for the US, partly because of a lower potential predictability of the European climate at seasonal time scales. Selection of extreme soil moisture conditions or a subset of models with similar initial soil moisture conditions does improve the forecast skill, and sporadic positive effects are also demonstrated for precipitation. Using realistic initial soil moisture data increases the interannual variability of temperature compared to the control simulations in the South-Central European area at longer lead times. This leads to better temperature forecasts in a remote area in Western Europe. However, the covered range of forecast dates (1986–1995) is too short to isolate a clear physical mechanism for this remote correlation.
KeywordsSoil moisture initialization Seasonal forecasting Potential predictability Europe
The contribution of land surface conditions to the predictability of meteorological features is of interest to a wide community. A major portion of predictability at monthly to seasonal time scales is attributed to anomalies in the sea surface temperature (SST), in particular those related to El Niño events (Kirtman and Pirani 2009). However, Koster et al. (2004) identified a number of key regions where anomalous soil moisture conditions may systematically affect precipitation variability in the boreal summer season, based on a model experiment involving multiple Global Circulation Models (GCMs). In combination with a realistic initialization of soil moisture and a long enough memory in the soil water reservoir, increased predictability may be feasible in these regions (Koster et al. 2010b). Dirmeyer et al. (2009) explored a systematic soil moisture–precipitation interaction using a range of observations and (offline) land models for all seasons, roughly confirming the existence of areas where adequate soil moisture information could lead to improved forecasts at the monthly to seasonal time scale. In general these areas are found in transitional zones between dry and wet climates, where the coupling between soil moisture and evapotranspiration is expected to be strong and large enough to affect climate (Koster et al. 2004). Several observational and modelling-based studies approximately agree on the location of these regions (Seneviratne et al. 2010).
Douville (2010) showed that soil moisture conditions in late spring played an important role in successfully modelling contrasting summers concerning precipitation and temperature in the Eurasian continent using a single GCM. A more systematic evaluation of the contribution of soil moisture to the forecast skill with up to two-month lead-time was presented by Koster et al. (2010a) in the context of the second Global Land–Atmosphere Coupling Experiment (GLACE2). This experiment consists of an extensive series of subseasonal ensemble forecasts with multiple models (see below for details). Concentrating on the North American area, the study showed that using realistic soil moisture initial conditions contributes to temperature forecast skill at subseasonal (2 months) lead-times. For precipitation, prediction skill was gained only when a sub-set of starting dates was selected based on the size of the initial soil moisture anomaly: more extreme soil conditions are found to have a stronger effect on the atmosphere than moderate or small anomalies. These results are consistent with those of Huang et al. (1996), who used observation-driven soil moisture anomaly estimates and statistical techniques to demonstrate the possible contribution of soil moisture anomalies to temperature prediction at multi-month time scales in the continental US.
This study evaluates GLACE2 results over Europe, another area where adequate observations permit a sound evaluation of skill. The metric analysed (proportion of explained variance of 2-week averaged standardized model outputs) is similar to the one presented by Koster et al. (2010a). The results are compared to the potential predictability, defined here as the ability of a collection of models to reproduce temperature or precipitation anomalies generated by any one model in this collection, which is treated as a pseudo-observation. This measure sets an upper limit on the skill improvement that can be expected from a multi-model experiment, bypassing the effect of systematic model biases with respect to observations. The potential predictability of temperature and precipitation in Europe differs significantly from that in the US, due to different characteristics of the variability and remote influences on the local climate (Rodwell and Doblas-Reyes 2006). We present first a brief outline of the general set-up of GLACE2 and the applied post-processing. This is followed by the main results.
2 Set-up of GLACE2, observations and diagnostics
2.1 The GLACE2 experiment
The multi-model experiment GLACE2 was designed to isolate the contribution of realistic soil moisture initialization to forecast skill of temperature and precipitation at lead-times of up to 60 days (Koster et al. 2010a). Each participating GCM produced two sets of 60-day, 10-member ensemble forecasts for 100 starting dates: the 1st and 15th day of the months between April and August of the years 1986–1995. The different ensemble members were generated using a range of different techniques by the different participants, depending on their technical constraints or preferred methods of ensemble generation; see Koster et al. (2010b) for details.
In the first set of forecasts (series 1), initial land surface states were extracted from a continuous offline land surface model simulation forced with observed precipitation, radiation, temperature, humidity and wind speed, as provided by the second Global Soil Wetness Project (GSWP2, Dirmeyer et al. 2006). This approach was followed because available in situ soil moisture information is not spatially comprehensive enough in itself to be useful for model initialization. Although the soil moisture fields generated by the offline models may substantially deviate from (highly localized) direct in situ observations (Guo et al. 2006), they generally do represent the effects of major anomalies in the hydrological conditions (precipitation, evaporation) that are captured by the offline forcing data. In addition, the modelled soil moisture products have the advantage of being consistent with the representation of soil moisture in the GCMs participating in GLACE2. In Series 2, initial land conditions were randomized, either by shuffling the GSWP2 fields (for a given day-of-year) in time, or by generating initial conditions for the day-of-year using a free climate run. In all experiments sea surface temperatures were prescribed during the 60-day forecasts. For this, an SST dataset was provided that was an estimate of the observed state on the start date of the forecast with a gradual relaxation to climatology as time proceeds. This set-up mimics the operational application of seasonal forecasting where future SSTs are derived from (uncertain) ocean model simulations (see Koster et al. (2010a, b) for details).
2.2 Data processing
GLACE2 participating models used for this study
2.8° × 2.8°
1.9° × 1.9°
1.4° × 1.4°
NCAR CAM 3.5
1.9° × 1.9°
Version 5; initial soil moisture series 1 derived from different land surface model simulations
1.1° × 1.1°a
Integrated Forecasting System (IFS), ocean–atmosphere coupled
1.9° × 1.9°
Soil initialization from data assimilation suite
1.1° × 1.1°a
As ECMWF, with prescribed sea surface temperatures
2.8° × 2.8°
0.9° × 0.9°
2.5° × 2°
GMAO forecasting system
We will refer to these normalized anomalies when discussing the temperature and precipitation results below. To avoid the effects of differences in atmospheric initialization or methods used to create the model ensemble members, results for the first 15-day period of each forecast are not analysed; we analyze instead the averages over days 16–30, days 31–45, and days 46–60 of each forecast. Similarly, for each day-of-year used as a forecast start date, the 15-day averages from the observations were expressed as standard normal deviates by calculating the mean and standard deviation of these averages over the 10 years in the sample.
2.3 Validation data sets
Observations over Europe were taken from the E-OBS data base (Haylock et al. 2008), in which carefully quality-checked station observations are gridded to 0.25° resolution. The observations were interpolated and time-averaged to the same grid and time axis as the model data. Care was taken to average the observations over the same calendar days as used for averaging the model output, implying slightly different intervals for different lead times. Over North America we used the data sets used by Koster et al. (2010a, b).
2.4 Predictability measures: forecast skill and potential predictability
To estimate the maximum possible value of land-derived skill that could be obtained from the multi-model experiment, we derived a measure of the “potential predictability”—R2 calculated as above, but instead of using the observations as the reference “truth”, we used the ensemble mean results from an individual model. This calculation was repeated using each of the ten models in turn as the reference truth, and the ten resulting score values were averaged after transforming them to a normal distribution using Fisher’s Z-score statistic 0.5 ln (1 + r)/(1 − r). Note that this metric is different from the average potential predictability calculated using the individual ensemble members as truth for every model separately. The procedure was applied to both the series 1 and series 2 simulations.
2.5 Data subsets for extreme soil moisture initializations
For various analyses presented below, a subset of forecasts was constructed based on initial soil moisture content. Extreme wet or dry soil moisture values were identified at each grid point from the 60 initial soil moisture conditions there (one for each start date providing data during the JJA period) by subtracting the mean seasonal cycle from the 60 values and then ranking the 60 anomalies. The extreme 20%-values refer to the 12 wettest and 12 driest start dates in the sample, and the 10%-values are the 6 wettest and 6 driest start dates. The fields used for this selection are a representative set of GSWP2-derived initial soil moisture fields, namely the fields constructed for the models ECMWF and KNMI, generated using the HTESSEL land surface model (Balsamo et al. 2009) which carries a 4-layer soil scheme. The total water content in the top three layers (top 1 m of soil) was taken as the grid point value. These soil moisture fields do represent the effects of anomalous hydrological forcings and are statistically very similar to the fields used by a majority of GLACE2 participants (see below).
The anomalies were calculated and ranked at each individual grid point to produce a subset of start dates specific to that grid point, ignoring the possible spatial coherence of the anomalies. In one analysis below, however, this coherence was retained by examining how the subset of start dates generated at one location affects the skill score generated in a predefined remote target domain.
3.1 Potential predictability
The term “potential predictability” is often interpreted as an intrinsic property of a geophysical system, expressing the degree to which chaos would limit forecast skill assuming a perfect model configuration. The predictability inherent in nature is not measurable; the best we can do is quantify the effects of chaos within a given model or set of models, for purposes of understanding better the models’ behaviour. Here, we estimate predictability from the ability of the multi-model simulations to predict the behaviour of a single participating model (Sect. 2.4).
From Fig. 1, it is also evident that the soil moisture related potential predictability increase is generally much higher in the US than in Europe. This is true for all lead times.
3.2 Forecast skill at different lead times
Reasonable fractions of potential predictability (>20%) are attained at short lead times in a major part of the European continent. This fraction drops with lead time, but less so in the Western half of Europe. Over the Iberian peninsula the fraction tends to increase, but this is at least partly an artefact of normalizing a low skill increase by a low potential predictability in that region. Within the limitations of the methodology followed here, Fig. 4 suggests that soil moisture initialization as implemented in the GLACE2 simulations does close the gap between actual and potential predictability at short lead times to some extent, and that more can be gained from other sources of skill, such as better model representations, higher resolutions, and improved datasets for the initialization and validation of the model variables.
3.3 Forecast skill for extreme initial soil moisture conditions
A supplemental analysis was performed in which the start dates were subsetted into two bins: those for which the initial soil moisture was lower than the climatological mean, and those for which it was higher. Temperature forecast skill levels were then computed for each subset to determine if drier conditions might lead to less (or more) skill than wetter conditions, in analogy to the analysis of Koster et al. (2010b). However, for the European area, no clear patterns of asymmetry were evident, and results are not shown.
3.4 Spatial patterns of initial soil moisture and forecast skill improvement
For temperature and—to some extent—precipitation forecasts for days 16–30, a great majority of the grid cells provide subsets of start dates for which land initialization contributes positively to skill in the Western European area. However, for precipitation, no grid cell provides a useful soil moisture subsetting for lead times longer than 4 weeks. For temperature, the 4–6 week and 6–8 week forecast in Western Europe is improved when the extreme soil moisture time slots are determined from Balkan and central-eastern European grid cells. Interestingly, this area is outside the domain in which the skill is improved, suggesting a potential physical or statistical connection between the two areas.
4 Discussion and conclusions
Results from the second Global Land Atmosphere Coupling Experiment (GLACE2) for Europe show that realistic soil moisture initialization in the spring and summer seasons does lead to improved forecast scores for temperature across the entire area at short lead times (16–30 days). At longer lead times the areas with improved scores decrease, and even some negative scores emerge at long lead times. The relatively low potential predictability in Europe may be related to the relatively large influence of remote (Atlantic) air masses on temperature and precipitation anomalies. Larger predictability and skill levels are seen in North America, perhaps due to the more continental (less maritime) nature of the climate there (especially in the central US), allowing soil moisture processes there to be more effective. In addition, the northern half of Europe is on average situated at higher latitudes with lower radiation levels (and thus lower evaporation and/or evaporation variability), and it contains fewer areas that might have soil moisture deficits.
As expected, the precipitation forecasts do not improve. Precipitation in most parts of Europe is dominated by atmospheric advection of moisture from the Atlantic (e.g. Van der Ent 2010), and local adjustments of soil moisture conditions may on average have a small impact on precipitation.
The contributions of realistic land initialization to skill in Europe are less pronounced than those shown by Koster et al. (2010a) for North America. The potential predictability at the time scales considered is lower in Europe than in North America, but in addition, the fraction of the potential predictability captured by the skill calculation is fairly low in Europe, particularly at long lead times, and with a systematic reduction of skill around the Baltic Sea. Although predictability metrics reflect model behaviour rather than intrinsic properties of the real climate, there may be ample room for improvement of the skill, particular through the use of better models, larger ensembles, sampling over a longer period, better initialization methods, and better observations. Koster et al. (2010b) already point at the limited quality of the soil moisture fields used to initialize the series 1 simulations in many areas of the world, largely a reflection of sparse rain gauge density. The verifying temperature and precipitation observations are also not free of errors, which will lead to a systematic gap between skill and potential predictability. Here we also show that the spread in the initial soil moisture content used for series 1 affects the multi-model skill: selecting a multi-model ensemble characterized by a high similarity in initial soil moisture gives better results.
As demonstrated for North America by Koster et al. (2010a), performing the skill calculations on subsets of the forecast periods as determined by the size of the initial soil moisture anomaly improves the skill scores in many areas of Europe. Soil moisture is not equally informative across the entire wetness range (Koster et al. 2009); selecting extreme soil moisture conditions apparently results in selecting moisture regimes that do affect evaporation and other atmospheric characteristics that in turn determine the surface temperature.
A suggestive result is that temperature forecast skill in Western Europe appears to be related to extreme soil moisture conditions in South-Central Europe. At longer lead times (46–60 days), computing skill for start dates subsetted on anomalous soil moisture conditions in the remote South-Central Europe region leads to larger skill levels in Western Europe. The South-Central Europe region (a “soil moisture initialization hotspot”) coincides with an area associated with strong soil moisture effects on the surface energy balance in climate simulations (Seneviratne et al. 2006) as well as with recent summer heat waves in regional climate simulations (Fischer et al. 2007). This area is also coincident with findings based on GSWP2 simulations and Fluxnet observations regarding the location of regions lying within the soil moisture-limited evapotranspiration regime in Europe (Teuling et al. 2009).
Even with the large number of simulations and models examined here, the noise level in this experiment is rather large. For the highly variable European climate, the 10-year time range covered by the GLACE2 experiment is too short to confirm the existence, for example, of clear atmospheric teleconnections via surface heat low development which can affect the circulation in a large domain. Using a 17-member ensemble climate simulation of 150 years duration, Haarsma et al. (2009) demonstrate an effect of a Mediterranean heat low development in response to excessive soil drying on atmospheric circulation at higher latitudes. This teleconnection could not be confirmed in the multi-model data set explored here, probably due to the limited number of weather situations covered in the experiment. To address such questions, we require an extended version of the GLACE2 experiment, covering a more comprehensive weather history—an experiment utilizing, for example, the multi-decadal forcing dataset of Sheffield et al. (2006) for the soil moisture initialization rather than the 10-year GSWP2 forcing dataset.
Help from Sarith Mahanama (NASA) and Frederic Vitart (ECMWF) for submitting data and executing simulations, as well as from the modelling teams participating in GLACE2, is highly appreciated. Alexander Bakker and Geert Jan van Oldenborgh helped design some of the statistical analyses. We acknowledge the E-OBS dataset from the EU-FP6 project ENSEMBLES (http://www.ensembles-eu.org) and the data providers in the ECA&D project (http://eca.knmi.nl).
This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and source are credited.
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