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Sem Vijverberg
,
Raed Hamed
, and
Dim Coumou

Abstract

Soy harvest failure events can severely impact farmers, insurance companies, and raise global prices. Reliable seasonal forecasts of misharvests would allow stakeholders to prepare and take appropriate early action. However, especially for farmers, the reliability and lead time of current prediction systems provide insufficient information to justify within-season adaptation measures. Recent innovations increased our ability to generate reliable statistical seasonal forecasts. Here, we combine these innovations to predict the 1–3 poor soy harvest years in the eastern United States. We first use a clustering algorithm to spatially aggregate crop producing regions within the eastern United States that are particularly sensitive to hot–dry weather conditions. Next, we use observational climate variables [sea surface temperature (SST) and soil moisture] to extract precursor time series at multiple lags. This allows the machine learning model to learn the low-frequency evolution, which carries important information for predictability. A selection based on causal inference allows for physically interpretable precursors. We show that the robust selected predictors are associated with the evolution of the horseshoe Pacific SST pattern, in line with previous research. We use the state of the horseshoe Pacific to identify years with enhanced predictability. We achieve high forecast skill of poor harvests events, even 3 months prior to sowing, using a strict one-step-ahead train-test splitting. Over the last 25 years, when the horseshoe Pacific SST pattern was anomalously strong, 67% of the poor harvests predicted in February were correct. When operational, this forecast would enable farmers to make informed decisions on adaption measures, for example, selecting more drought-resistant cultivars or change planting management.

Significance Statement

If soy farmers would know that the upcoming growing season will be hot and dry, they could decide to take anticipatory action to reduce losses, that is, buy more drought resistant soy cultivars or change planting management. To make such decisions, farmers would need information even prior to sowing. On these very long lead times, a predictable signal can emerge from low-frequency processes of the climate system that can affect surface weather via teleconnections. However, traditional forecast systems are unable to make reliable predictions at these lead times. In this work, we used machine learning techniques to train a forecast model based on these low-frequency components. This allowed us to make reliable predictions of poor harvest years even 3 months prior to sowing.

Open access
Ruud Sperna Weiland
,
Karin van der Wiel
,
Frank Selten
, and
Dim Coumou

Abstract

Persistent hot–dry or cold–wet summer weather can have significant impacts on agriculture, health, and the environment. For northwestern Europe, these weather regimes are typically linked to, respectively, blocked or zonal jet stream states. The fundamental dynamics underlying these circulation states are still poorly understood. Edward Lorenz postulated that summer circulation may be either fully or almost intransitive, implying that part of the phase space (capturing circulation variability) cannot be reached within one specific summer. If true, this would have major implications for the predictability of summer weather and our understanding of the drivers of interannual variability of summer weather. Here, we test the two Lorenz hypotheses (i.e., fully or almost intransitive) for European summer circulation, capitalizing on a newly available very large ensemble (2000 years) of present-day climate data in the fully coupled global climate model EC-Earth. Using self-organizing maps, we quantify the phase space of summer circulation and the trajectories through phase space in unprecedented detail. We show that, based on Markov assumptions, the summer circulation is strongly dependent on its initial state in early summer with the atmospheric memory ranging from 28 days up to ~45 days. The memory is particularly long if the initial state is either a blocked or a zonal flow state. Furthermore, we identify two groups of summers that are characterized by distinctly different trajectories through phase space, and that prefer either a blocked or zonal circulation state, respectively. These results suggest that intransitivity is indeed a fundamental property of the atmosphere and an important driver of interannual variability.

Open access
Marlene Kretschmer
,
Dim Coumou
,
Jonathan F. Donges
, and
Jakob Runge

Abstract

In recent years, the Northern Hemisphere midlatitudes have suffered from severe winters like the extreme 2012/13 winter in the eastern United States. These cold spells were linked to a meandering upper-tropospheric jet stream pattern and a negative Arctic Oscillation index (AO). However, the nature of the drivers behind these circulation patterns remains controversial. Various studies have proposed different mechanisms related to changes in the Arctic, most of them related to a reduction in sea ice concentrations or increasing Eurasian snow cover.

Here, a novel type of time series analysis, called causal effect networks (CEN), based on graphical models is introduced to assess causal relationships and their time delays between different processes. The effect of different Arctic actors on winter circulation on weekly to monthly time scales is studied, and robust network patterns are found. Barents and Kara sea ice concentrations are detected to be important external drivers of the midlatitude circulation, influencing winter AO via tropospheric mechanisms and through processes involving the stratosphere. Eurasia snow cover is also detected to have a causal effect on sea level pressure in Asia, but its exact role on AO remains unclear. The CEN approach presented in this study overcomes some difficulties in interpreting correlation analyses, complements model experiments for testing hypotheses involving teleconnections, and can be used to assess their validity. The findings confirm that sea ice concentrations in autumn in the Barents and Kara Seas are an important driver of winter circulation in the midlatitudes.

Full access
Sem Vijverberg
,
Maurice Schmeits
,
Karin van der Wiel
, and
Dim Coumou

Abstract

Extreme summer temperatures can cause severe societal impacts. Early warnings can aid societal preparedness, but reliable forecasts for extreme temperatures at subseasonal-to-seasonal (S2S) time scales are still missing. Earlier work showed that specific sea surface temperature (SST) patterns over the northern Pacific Ocean are precursors of high temperature events in the eastern United States, which might provide skillful forecasts at long leads (~50 days). However, the verification was based on a single skill metric, and a probabilistic forecast was missing. Here, we introduce a novel algorithm that objectively extracts robust precursors from SST linked to a binary target variable. When applied to reanalysis (ERA-5) and climate model data (EC-Earth), we identify robust precursors with the clearest links over the North Pacific. Different precursors are tested as input for a statistical model to forecast high temperature events. Using multiple skill metrics for verification, we show that daily high temperature events have no predictive skill at long leads. By systematically testing the influence of temporal and spatial aggregation, we find that noise in the target time series is an important bottleneck for predicting extreme events on S2S time scales. We show that skill can be increased by a combination of 1) aggregating spatially and/or temporally, 2) lowering the threshold of the target events to increase the base rate, or 3) adding additional variables containing predictive information (soil moisture). Exploiting these skill-enhancing factors, we obtain forecast skill for moderate heat waves (i.e., 2 or more hot days closely clustered together in time) with up to 50 days of lead time.

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
Marlene Kretschmer
,
Dim Coumou
,
Laurie Agel
,
Mathew Barlow
,
Eli Tziperman
, and
Judah Cohen

Abstract

The extratropical stratosphere in boreal winter is characterized by a strong circumpolar westerly jet, confining the coldest temperatures at high latitudes. The jet, referred to as the stratospheric polar vortex, is predominantly zonal and centered around the pole; however, it does exhibit large variability in wind speed and location. Previous studies showed that a weak stratospheric polar vortex can lead to cold-air outbreaks in the midlatitudes, but the exact relationships and mechanisms are unclear. Particularly, it is unclear whether stratospheric variability has contributed to the observed anomalous cooling trends in midlatitude Eurasia. Using hierarchical clustering, we show that over the last 37 years, the frequency of weak vortex states in mid- to late winter (January and February) has increased, which was accompanied by subsequent cold extremes in midlatitude Eurasia. For this region, 60% of the observed cooling in the era of Arctic amplification, that is, since 1990, can be explained by the increased frequency of weak stratospheric polar vortex states, a number that increases to almost 80% when El Niño–Southern Oscillation (ENSO) variability is included as well.

Full access
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
Gabriele Messori
,
Emanuele Bevacqua
,
Rodrigo Caballero
,
Dim Coumou
,
Paolo De Luca
,
Davide Faranda
,
Kai Kornhuber
,
Olivia Martius
,
Flavio Pons
,
Colin Raymond
,
Kunhui Ye
,
Pascal Yiou
, and
Jakob Zscheischler
Open access