The Possible Reasons for the Misrepresented Long-Term Climate Trends in the Seasonal Forecasts of HFP2

Abstract The climate trend in a dynamical seasonal forecasting system is examined using 33-yr multimodel ensemble (MME) forecasts from the second phase of the Canadian Historical Forecasting Project (HFP2). It is found that the warming trend of the seasonal forecast in March–May (MAM) over the Eurasian continent is in a good agreement with that in the observations. However, the seasonal forecast failed to reproduce the observed pronounced surface air temperature (SAT) trend in December–February (DJF). The possible reasons responsible for the different behaviors of the HFP2 models in MAM and DJF are investigated. Results show that the initial conditions used for the HFP2 forecast system in MAM have a warming trend over the Eurasian continent, which may come from high-frequency weather systems, whereas the initial conditions for the DJF seasonal forecast do not have such a trend. This trend in the initial condition contributes to the trend of the seasonal forecast in the first month. On the other hand, an examination of the lower boundary SST anomaly forcing shows that the SST trend in MAM has a negative SST anomaly along the central equatorial Pacific, which is favorable for a positive phase of the North Atlantic Oscillation atmospheric response and a warming over the Eurasian continent. The long-term SST trend used for the seasonal forecast in DJF, however, has a negative trend in the tropical eastern Pacific, which is associated with a Pacific–North American pattern–like atmospheric response that has little contribution to a warming in the Eurasian continent.

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Transient Atmospheric Response to Interactive SST Anomalies

Journal of Climate ◽

10.1175/2007jcli1704.1 ◽

2008 ◽

Vol 21 (3) ◽

pp. 576-583 ◽

Cited By ~ 13

Author(s):

David Ferreira ◽

Claude Frankignoul

Keyword(s):

North Atlantic ◽

Initial Conditions ◽

Maximum Amplitude ◽

Atmospheric Response ◽

The North ◽

Winter Conditions ◽

Initial Evolution ◽

Sst Anomaly ◽

The North Atlantic ◽

Sst Anomalies

Abstract The transient atmospheric response to interactive SST anomalies in the midlatitudes is investigated using a three-layer QG model coupled in perpetual winter conditions to a slab oceanic mixed layer in the North Atlantic. The SST anomalies are diagnosed from a coupled run and prescribed as initial conditions, but are free to evolve. The initial evolution of the atmospheric response is similar to that obtained with a prescribed SST anomaly, starting as a quasi-linear baroclinic and then quickly evolving into a growing equivalent barotropic one. Because of the heat flux damping, the SST anomaly amplitude slowly decreases, albeit with little change in pattern. Correspondingly, the atmospheric response only increases until it reaches a maximum amplitude after about 1–3.5 months, depending on the SST anomaly considered. The response is similar to that at equilibrium in the fixed SST case, but it is 1.5–2 times smaller, and then slowly decays away.

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Seasonal Forecasts of the Saharan Heat Low characteristics: A multi-model assessment

10.5194/wcd-2021-19 ◽

2021 ◽

Author(s):

Cedric G. Ngoungue Langue ◽

Christophe Lavaysse ◽

Mathieu Vrac ◽

Philippe Peyrille ◽

Cyrille Flamant

Keyword(s):

Monsoon Season ◽

West African Monsoon ◽

Warming Trend ◽

Seasonal Forecast ◽

Model Assessment ◽

Seasonal Forecasts ◽

Modes Of Variability ◽

Forecast Models ◽

Sahel Region ◽

Heat Low

Abstract. The Saharan Heat Low (SHL) is a key component of the West African monsoon system at synoptic scale and a driver of summertime precipitation over the Sahel region. Therefore, accurate seasonal precipitation forecasts rely in part on a proper representation of the SHL characteristics in seasonal forecasts models. This is investigated using the last versions of two seasonal forecast systems namely the SEAS5 and MF7 systems respectively from the European Center of Medium range Weather Forecasts (ECMWF) and Meteo-France. The SHL characteristics in the seasonal forecast models is assessed based on a comparison with the fifth ECMWF ReAnalysis (ERA5) for the period 1993–2016. The analysis of the modes of variability shows that the seasonal forecast models have issues with the timing of the SHL pulsations and the intensities when compared to ERA5. SEAS5 and MF7 show a cooling trend centered on the Sahara and a warming trend located in the eastern part of the Sahara, respectively. Both models tend to under-estimate the inter-annual variability of the SHL. We also show that the seasonal forecast models detect the eastward and westward shift of the SHL during the monsoon season. The use of statistical bias correction methods significantly reduces the bias in the seasonal forecast models and improves the forecast score. Despite an improvement of prediction score, the SHL-related forecast skills of SEAS5 and MF7 remain weak for a lead time beyond 1 month.

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Seasonal forecast verification and application in times of change

10.5194/esd-2016-60 ◽

2016 ◽

Author(s):

Yoav Levi ◽

Itzhak Carmona

Keyword(s):

Warming Trend ◽

Seasonal Forecast ◽

Forecast Skill ◽

Decision Makers ◽

Economic Environment ◽

Boreal Summer ◽

Seasonal Forecasts ◽

Climate Services ◽

The Past ◽

Skill Scores

Abstract. Seasonal forecast is being promoted as one of the climate services given to the public and decision makers also in the extra-tropics. However seasonal forecast is a scientific challenge. Rapid changes in climate and the socio-economic environment in the past 30 years introduce even a bigger challenge for the end-users of seasonal forecasts based on the past 30 years. Decision makers should relay on a forecast only if they fully understand the forecast skill and the forecast will not be a completely erroneous.Therefore, the percentage of forecasts for above normal condition that realized to be below normal conditions and vice versa is measured straightforwardly by the "Fiasco score". To overcome the climate and socio-economic environment changes an attempt to relate the next seasonal forecast to the previous season forecast and observed values was tested.The findings indicate that ECMWF system-4 seasonal forecast skill for June-July-August (JJA) temperatures for the marine tropics is very promising as indicated by all the skill scores, including using the previous JJA forecast as the base for the next JJA season. However for the boreal summer temperatures forecast over land, the main source of the model predictability originates from the warming trend along the hindcast period. Over the Middle East and Mongolia removing the temperature trend eliminated the high forecast skill. Evaluation of the ability of the next season forecast to predict the changes relative to the previous year's season has shown a positive skill in some areas compared to the traditional 30 years based climatology after both forecasts and observed data were de-trend.

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The Role of Long-Term Trends in Seasonal Predictions: Implication of Global Warming in the NCEP CFS

Weather and Forecasting ◽

10.1175/2009waf2222231.1 ◽

2009 ◽

Vol 24 (4) ◽

pp. 965-973 ◽

Cited By ~ 14

Author(s):

Ming Cai ◽

Chul-Su Shin ◽

H. M. van den Dool ◽

Wanqiu Wang ◽

S. Saha ◽

...

Keyword(s):

Greenhouse Gases ◽

Surface Air Temperature ◽

Seasonal Prediction ◽

Warming Trend ◽

Cold Bias ◽

Seasonal Forecasts ◽

Warm Bias ◽

Seasonal Climate ◽

Skill Analysis

Abstract This paper analyzes long-term surface air temperature trends in a 25-yr (1982–2006) dataset of retrospective seasonal climate predictions made by the NCEP Climate Forecast System (CFS), a model that has its atmospheric greenhouse gases fixed at the 1988 concentration level. Although the CFS seasonal forecasts tend to follow the observed interannual variability very closely, there exists a noticeable time-dependent discrepancy between the CFS forecasts and observations, with a warm model bias before 1988 and a cold bias afterward except for a short-lived warm bias during 1992–94. The trend from warm to cold biases is likely caused by not including the observed increase in the anthropogenic greenhouse gases in the CFS, whereas the warm bias in 1992–94 reflects the absence of the anomalous aerosols released by the 1991 Mount Pinatubo eruption. Skill analysis of the CFS seasonal climate predictions with and without the warming trend suggests that the 1997–98 El Niño event contributes significantly to the record-breaking global warmth in 1998 whereas the record-breaking warm decade since 2000 is mainly due to the effects of the increased greenhouse gases. Implications for operational seasonal prediction will be discussed.

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Verification of ECMWF System4 for seasonal hydrological forecasting in a northern climate

10.5194/hess-2017-387 ◽

2017 ◽

Cited By ~ 1

Author(s):

Rachel Bazile ◽

Marie-Amélie Boucher ◽

Luc Perreault ◽

Robert Leconte

Keyword(s):

Climate Models ◽

Initial Conditions ◽

Meteorological Data ◽

Skill Score ◽

Lead Times ◽

Seasonal Forecasts ◽

Hydro Power ◽

Hydrological Forecasting ◽

Northern Climate

Abstract. Hydro-power production requires optimal dam management. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Center for Medium-Range Forecast)'s System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of Continuous Ranked Probability Score. Then, three seasonal ensemble hydrological forecasting systems are compared: 1) the climatology of simulated streamflow, 2) the ensemble hydrological forecasts based on climatology (ESP) and 3) the hydrological forecasts based on bias-corrected ensemble meteorological fore- casts from System4 (corr-DSP). Simulated streamflows are used as observations. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead-times from 1-month to 5-month depending on the season and watershed. Corr-DSP appears quite reliable but sometimes suffer from under- dispersion. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead-time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts per- formance for spring is close to the performance of ESP. For longer lead-times, results are mixed and the CRPS skill score is close to 0 in most cases. Bias-corrected ensemble meteorological forecasts appear to be an interesting source of information for hydrological forecasting.

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Seasonal Forecast of Non-monsoonal Winter Precipitation over the Eurasian Continent using Machine Learning Models

Journal of Climate ◽

10.1175/jcli-d-21-0113.1 ◽

2021 ◽

pp. 1-42

Author(s):

QiFeng Qian ◽

XiaoJing Jia ◽

Hai Lin ◽

Ruizhi Zhang

Keyword(s):

Machine Learning ◽

Dynamic Models ◽

Winter Precipitation ◽

Seasonal Forecast ◽

Forecast Skill ◽

The Arctic ◽

Gradient Boosting ◽

Seasonal Forecasts ◽

Eurasian Continent ◽

Extreme Gradient Boosting

AbstractIn this study, four machine learning (ML) models (gradient boost decision tree (GBDT), light gradient boosting machine (LightGBM), categorical boosting (CatBoost) and extreme gradient boosting (XGBoost)) are used to perform seasonal forecasts for non-monsoonal winter precipitation over the Eurasian continent (30-60°N, 30-105°E) (NWPE). The seasonal forecast results from a traditional linear regression (LR) model and two dynamic models are compared. The ML and LR models are trained using the data for the period of 1979-2010, and then, these empirical models are used to perform the seasonal forecast of NWPE for 2011-2018. Our results show that the four ML models have reasonable seasonal forecast skills for the NWPE and clearly outperform the LR model. The ML models and the dynamic models have skillful forecasts for the NWPE over different regions. The ensemble means of the forecasts including the ML models and dynamic models show higher forecast skill for the NWEP than the ensemble mean of the dynamic-only models. The forecast skill of the ML models mainly benefits from a skillful forecast of the third empirical orthogonal function (EOF) mode (EOF3) of the NWPE, which has a good and consistent prediction among the ML models. Our results also illustrate that the sea ice over the Arctic in the previous autumn is the most important predictor in the ML models in forecasting the NWPE. This study suggests that ML models may be useful tools to help improve seasonal forecasts of the NWPE.

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Seasonal forecast of water resources over the Euro-Mediterranean region

10.5194/egusphere-egu2020-20246 ◽

2020 ◽

Author(s):

Gildas Dayon ◽

François Besson ◽

Jean-Michel Soubeyroux ◽

Chrisitian Viel ◽

Paola Marson

Keyword(s):

Mediterranean Region ◽

Snow Water Equivalent ◽

Seasonal Forecast ◽

Added Value ◽

Seasonal Forecasts ◽

Forecast System ◽

Physically Based Model ◽

Physically Based ◽

Snow Water

In the framework of the MEDSCOPE project, a forecasting chain is developed at M&#233;t&#233;o-France for hydrological long term predictions over the Euro-Mediterranean region, from one month up to seven months. This new prototype is based on the M&#233;t&#233;o-France System 6 global seasonal forecast system. Atmospheric forecasts are interpolated to 5.5 km and corrected by the statistical method ADAMONT using the UERRA regional atmospheric reanalysis as reference. These high resolution forecasts drive the physically-based model SURFEX coupled to CTRIP providing seasonal forecasts of surface variables : river discharges, soil wetness indices, snow water equivalent.A forecast using the climatology (ESP approach) has been produced on the period 1993-2016. It is use to explore the sources of predictability in the different watersheds (Ebro, Po, Rh&#244;ne). Predictability is mostly coming from the snow pack built during the winter and the soil moisture evolution in spring and summer. A hindcast on the period 1993-2016 is produced to assess the added value of the seasonal forecast compared to the climatology for the end-users in agriculture and energy.

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The Indian Ocean: The Region of Highest Skill Worldwide in Decadal Climate Prediction*

Journal of Climate ◽

10.1175/jcli-d-12-00049.1 ◽

2013 ◽

Vol 26 (3) ◽

pp. 726-739 ◽

Cited By ~ 34

Author(s):

Virginie Guemas ◽

Susanna Corti ◽

J. García-Serrano ◽

F. J. Doblas-Reyes ◽

Magdalena Balmaseda ◽

...

Keyword(s):

Indian Ocean ◽

Initial Conditions ◽

Internal Variability ◽

Warming Trend ◽

Radiative Forcings ◽

The Indian Ocean ◽

Volcanic Aerosols ◽

Decadal Climate ◽

High Skill

Abstract The Indian Ocean stands out as the region where the state-of-the-art decadal climate predictions of sea surface temperature (SST) perform the best worldwide for forecast times ranging from the second to the ninth year, according to correlation and root-mean-square error (RMSE) scores. This paper investigates the reasons for this high skill by assessing the contributions from the initial conditions, greenhouse gases, solar activity, and volcanic aerosols. The comparison between the SST correlation skill in uninitialized historical simulations and hindcasts initialized from estimates of the observed climate state shows that the high Indian Ocean skill is largely explained by the varying radiative forcings, the latter finding being supported by a set of additional sensitivity experiments. The long-term warming trend is the primary contributor to the high skill, though not the only one. Volcanic aerosols bring additional skill in this region as shown by the comparison between initialized hindcasts taking into account or not the effect of volcanic stratospheric aerosols and by the drop in skill when filtering out their effect in hindcasts that take them into account. Indeed, the Indian Ocean is shown to be the region where the ratio of the internally generated over the externally forced variability is the lowest, where the amplitude of the internal variability has been estimated by removing the effect of long-term warming trend and volcanic aerosols by a multiple least squares linear regression on observed SSTs.

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On the Correspondence between Seasonal Forecast Biases and Long-Term Climate Biases in Sea Surface Temperature

Journal of Climate ◽

10.1175/jcli-d-20-0338.1 ◽

2020 ◽

Vol 34 (1) ◽

pp. 427-446

Author(s):

Hsi-Yen Ma ◽

A. Cheska Siongco ◽

Stephen A. Klein ◽

Shaocheng Xie ◽

Alicia R. Karspeck ◽

...

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Time Scales ◽

Initial Conditions ◽

Global Climate Models ◽

Sea Surface ◽

Substantial Part ◽

Seasonal Forecasts ◽

Climate Simulations

AbstractThe correspondence between mean sea surface temperature (SST) biases in retrospective seasonal forecasts (hindcasts) and long-term climate simulations from five global climate models is examined to diagnose the degree to which systematic SST biases develop on seasonal time scales. The hindcasts are from the North American Multimodel Ensemble, and the climate simulations are from the Coupled Model Intercomparison Project. The analysis suggests that most robust climatological SST biases begin to form within 6 months of a realistically initialized integration, although the growth rate varies with location, time, and model. In regions with large biases, interannual variability and ensemble spread is much smaller than the climatological bias. Additional ensemble hindcasts of the Community Earth System Model with a different initialization method suggest that initial conditions do matter for the initial bias growth, but the overall global bias patterns are similar after 6 months. A hindcast approach is more suitable to study biases over the tropics and subtropics than over the extratropics because of smaller initial biases and faster bias growth. The rapid emergence of SST biases makes it likely that fast processes with time scales shorter than the seasonal time scales in the atmosphere and upper ocean are responsible for a substantial part of the climatological SST biases. Studying the growth of biases may provide important clues to the causes and ultimately the amelioration of these biases. Further, initialized seasonal hindcasts can profitably be used in the development of high-resolution coupled ocean–atmosphere models.

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Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate

Hydrology and Earth System Sciences ◽

10.5194/hess-21-5747-2017 ◽

2017 ◽

Vol 21 (11) ◽

pp. 5747-5762 ◽

Cited By ~ 8

Author(s):

Rachel Bazile ◽

Marie-Amélie Boucher ◽

Luc Perreault ◽

Robert Leconte

Keyword(s):

Bias Correction ◽

Climate Models ◽

Climate Model ◽

Initial Conditions ◽

Meteorological Data ◽

Lead Times ◽

Seasonal Forecasts ◽

Hydrological Forecasting ◽

Northern Climate

Abstract. Hydropower production requires optimal dam and reservoir management to prevent flooding damage and avoid operation losses. In a northern climate, where spring freshet constitutes the main inflow volume, seasonal forecasts can help to establish a yearly strategy. Long-term hydrological forecasts often rely on past observations of streamflow or meteorological data. Another alternative is to use ensemble meteorological forecasts produced by climate models. In this paper, those produced by the ECMWF (European Centre for Medium-Range Forecast) System 4 are examined and bias is characterized. Bias correction, through the linear scaling method, improves the performance of the raw ensemble meteorological forecasts in terms of continuous ranked probability score (CRPS). Then, three seasonal ensemble hydrological forecasting systems are compared: (1) the climatology of simulated streamflow, (2) the ensemble hydrological forecasts based on climatology (ESP) and (3) the hydrological forecasts based on bias-corrected ensemble meteorological forecasts from System 4 (corr-DSP). Simulated streamflow computed using observed meteorological data is used as benchmark. Accounting for initial conditions is valuable even for long-term forecasts. ESP and corr-DSP both outperform the climatology of simulated streamflow for lead times from 1 to 5 months depending on the season and watershed. Integrating information about future meteorological conditions also improves monthly volume forecasts. For the 1-month lead time, a gain exists for almost all watersheds during winter, summer and fall. However, volume forecasts performance for spring varies from one watershed to another. For most of them, the performance is close to the performance of ESP. For longer lead times, the CRPS skill score is mostly in favour of ESP, even if for many watersheds, ESP and corr-DSP have comparable skill. Corr-DSP appears quite reliable but, in some cases, under-dispersion or bias is observed. A more complex bias-correction method should be further investigated to remedy this weakness and take more advantage of the ensemble forecasts produced by the climate model. Overall, in this study, bias-corrected ensemble meteorological forecasts appear to be an interesting source of information for hydrological forecasting for lead times up to 1 month. They could also complement ESP for longer lead times.

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