Showing papers by "Gianpaolo Balsamo published in 2019"
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TL;DR: SEAS5 as discussed by the authors is the ECMWF's fifth generation seasonal forecast system, which became operational in November 2017 and includes upgraded versions of the atmosphere and ocean models at higher resolutions and adds a prognostic sea-ice model.
Abstract: . In this paper we describe SEAS5, ECMWF's fifth generation seasonal forecast system, which became operational in November 2017. Compared to its predecessor, System 4, SEAS5 is a substantially changed forecast system. It includes upgraded versions of the atmosphere and ocean models at higher resolutions, and adds a prognostic sea-ice model. Here, we describe the configuration of SEAS5 and summarise the most noticeable results from a set of diagnostics including biases, variability, teleconnections and forecast skill. An important improvement in SEAS5 is the reduction of the equatorial Pacific cold tongue bias, which is accompanied by a more realistic El Nino amplitude and an improvement in El Nino prediction skill over the central-west Pacific. Improvements in 2 m temperature skill are also clear over the tropical Pacific. Sea-surface temperature (SST) biases in the northern extratropics change due to increased ocean resolution, especially in regions associated with western boundary currents. The increased ocean resolution exposes a new problem in the northwest Atlantic, where SEAS5 fails to capture decadal variability of the North Atlantic subpolar gyre, resulting in a degradation of DJF 2 m temperature prediction skill in this region. The prognostic sea-ice model improves seasonal predictions of sea-ice cover, although some regions and seasons suffer from biases introduced by employing a fully dynamical model rather than the simple, empirical scheme used in System 4. There are also improvements in 2 m temperature skill in the vicinity of the Arctic sea-ice edge. Cold temperature biases in the troposphere improve, but increase at the tropopause. Biases in the extratropical jets are larger than in System 4: extratropical jets are too strong, and displaced northwards in JJA. In summary, development and added complexity since System 4 has ensured that SEAS5 is a state-of-the-art seasonal forecast system which continues to display a particular strength in the El Nino Southern Oscillation (ENSO) prediction.
340 citations
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TL;DR: In this article, the authors compare the snow depth and snow cover in global reanalyses with satellite and in situ data in the Tibetan Plateau (TP) region and conclude that excessive snowfall might be the primary factor for the large overestimation of snow depth.
Abstract: . The Tibetan Plateau (TP) region, often referred to as the Third
Pole, is the world's highest plateau and exerts a considerable influence on
regional and global climate. The state of the snowpack over the TP is a
major research focus due to its great impact on the headwaters of a dozen
major Asian rivers. While many studies have attempted to validate
atmospheric reanalyses over the TP area in terms of temperature or
precipitation, there have been – remarkably – no studies aimed at
systematically comparing the snow depth or snow cover in global reanalyses
with satellite and in situ data. Yet, snow in reanalyses provides critical
surface information for forecast systems from the medium to sub-seasonal
timescales. Here, snow depth and snow cover from four recent global reanalysis products, namely the European Centre for
Medium-Range Weather Forecasts (ECMWF) ERA5 and ERA-Interim reanalyses, the
Japanese 55-year Reanalysis (JRA-55) and the NASA Modern-Era Retrospective analysis
for Research and Applications (MERRA-2), are
inter-compared over the TP region. The reanalyses are evaluated
against a set of 33 in situ station observations, as well as against the
Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and
a satellite microwave snow depth dataset. The high temporal correlation
coefficient (0.78) between the IMS snow cover and the in situ observations
provides confidence in the station data despite the relative paucity of
in situ measurement sites and the harsh operating conditions. While several reanalyses show a systematic overestimation of the snow
depth or snow cover, the reanalyses that assimilate local in situ
observations or IMS snow cover are better capable of representing the
shallow, transient snowpack over the TP region. The latter point is clearly
demonstrated by examining the family of reanalyses from the ECMWF, of which
only the older ERA-Interim assimilated IMS snow cover at high altitudes,
while ERA5 did not consider IMS snow cover for high altitudes. We further
tested the sensitivity of the ERA5-Land model in offline experiments,
assessing the impact of blown snow sublimation, snow cover to snow depth
conversion and, more importantly, excessive snowfall. These results suggest
that excessive snowfall might be the primary factor for the large
overestimation of snow depth and cover in ERA5 reanalysis. Pending a
solution for this common model precipitation bias over the Himalayas and the TP,
future snow reanalyses that optimally combine the use of satellite snow
cover and in situ snow depth observations in the assimilation and analysis
cycles have the potential to improve medium-range to sub-seasonal forecasts
for water resources applications.
115 citations
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Lamont–Doherty Earth Observatory1, University of Exeter2, Hadley Centre for Climate Prediction and Research3, Iowa State University4, European Centre for Medium-Range Weather Forecasts5, Environment Canada6, Barcelona Supercomputing Center7, University of Reading8, University of Tokyo9, World Meteorological Organization10, Max Planck Society11, University of Kiel12, Leibniz Institute of Marine Sciences13, Deutscher Wetterdienst14, Hobart Corporation15, Cooperative Institute for Research in Environmental Sciences16, Earth System Research Laboratory17, Bureau of Meteorology18, University of California, Los Angeles19, Japan Meteorological Agency20, Chinese Academy of Sciences21
TL;DR: In this paper, the authors discuss how to bridge the gap between current seasonal forecasts and century-scale climate change projections, allowing a seamless climate service delivery chain to be established, and outline concrete steps towards the provision of operational near-term climate predictions.
Abstract: Near-term climate predictions — which operate on annual to decadal timescales — offer benefits for climate adaptation and resilience, and are thus important for society. Although skilful near-term predictions are now possible, particularly when coupled models are initialized from the current climate state (most importantly from the ocean), several scientific challenges remain, including gaps in understanding and modelling the underlying physical mechanisms. This Perspective discusses how these challenges can be overcome, outlining concrete steps towards the provision of operational near-term climate predictions. Progress in this endeavour will bridge the gap between current seasonal forecasts and century-scale climate change projections, allowing a seamless climate service delivery chain to be established.
114 citations
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TL;DR: Results suggest that land surface spatial resolution is key (e.g., associated to a better representation of the land cover, topography) and using HRES forcing still enhances the skill and there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis.
Abstract: This study aims to assess the potential of the LDAS-Monde platform, a land data assimilation system developed by Meteo-France, to monitor the impact on vegetation state of the 2018 summer heatwave over Western Europe. The LDAS-Monde is driven by ECMWF’s (i) ERA5 reanalysis, and (ii) the Integrated Forecasting System High Resolution operational analysis (IFS-HRES), used in conjunction with the assimilation of Copernicus Global Land Service (CGLS) satellite-derived products, namely the Surface Soil Moisture (SSM) and the Leaf Area Index (LAI). The study of long time series of satellite derived CGLS LAI (2000–2018) and SSM (2008–2018) highlights marked negative anomalies for July 2018 affecting large areas of northwestern Europe and reflects the impact of the heatwave. Such large anomalies spreading over a large part of the domain of interest have never been observed in the LAI product over this 19-year period. LDAS-Monde land surface reanalyses were produced at spatial resolutions of 0.25° × 0.25° (January 2008 to October 2018) and 0.10° × 0.10° (April 2016 to December 2018). Both configurations of LDAS-Monde forced by either ERA5 or HRES capture well the vegetation state in general and for this specific event, with HRES configuration exhibiting better monitoring skills than ERA5 configuration. The consistency of ERA5- and IFS HRES-driven simulations over the common period (April 2016 to October 2018) allowed to disentangle and appreciate the origin of improvements observed between the ERA5 and HRES. Another experiment, down-scaling ERA5 to HRES spatial resolutions, was performed. Results suggest that land surface spatial resolution is key (e.g., associated to a better representation of the land cover, topography) and using HRES forcing still enhances the skill. While there are advantages in using HRES, there is added value in down-scaling ERA5, which can provide consistent, long term, high resolution land reanalysis. If the improvement from LDAS-Monde analysis on control variables (soil moisture from layers 2 to 8 of the model representing the first meter of soil and LAI) from the assimilation of SSM and LAI was expected, other model variables benefit from the assimilation through biophysical processes and feedback in the model. Finally, we also found added value of initializing 8-day land surface HRES driven forecasts from LDAS-Monde analysis when compared with model-only initial conditions.
48 citations
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35 citations
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TL;DR: In this paper, the authors apply spectral empirical orthogonal function (SEOF) analysis to derive climate patterns as dominant spatiotemporal modes of variability from reanalysis data.
Abstract: We apply spectral empirical orthogonal function (SEOF) analysis to educe climate patterns as dominant spatiotemporal modes of variability from reanalysis data. SEOF is a frequency-domain va...
23 citations
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01 Jan 2019
TL;DR: In this article, the role of land-surface interactions with the atmosphere on sub-seasonal timescales is discussed in terms of the physical processes as they are currently understood, as well as the implications for improved predictions.
Abstract: In this chapter, the role of land-surface interactions with the atmosphere on sub-seasonal timescales is discussed in terms of the physical processes as they are currently understood, as well as the implications for improved predictions. This potential stems from the predictability provided by relatively slowly varying land-surface states like soil moisture, snow cover, and vegetation states. A history of the evolution of land-surface models at operational forecast centers is also provided. We conclude that significant improvements in forecast skill can be made in the short term by treating land and atmosphere as a coupled system throughout the model development process, applying available observations to better calibrate, validate, and initialize land-surface states. Sub-seasonal to seasonal (S2S) prediction time scales are a potentially strong target for land-atmosphere feedbacks to affect the atmosphere.
19 citations
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TL;DR: In this article, new datasets and methods for generating lake fraction and lake depth fields for the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF) model are proposed.
Abstract: . Water bodies influence local weather and climate,
especially in lake-rich areas. The FLake (Fresh-water Lake model)
parameterisation is employed in the Integrated Forecasting System (IFS) of the
European Centre for Medium-Range Weather Forecasts (ECMWF) model which is
used operationally to produce global weather predictions. Lake depth and
lake fraction are the main driving parameters in the FLake parameterisation.
The lake parameter fields for the IFS should be global and realistic, because
FLake runs over all the grid boxes, and then only lake-related results are
used further. In this study new datasets and methods for generating lake
fraction and lake depth fields for the IFS are proposed. The data include the
new version of the Global Lake Database (GLDBv3) which contains depth
estimates for unstudied lakes based on a geological approach, the General
Bathymetric Chart of the Oceans and the Global Surface Water Explorer
dataset which contains information on the spatial and temporal variability
of surface water. The first new method suggested is a two-step lake fraction
calculation; the first step is at 1 km grid resolution and the second is at
the resolution of other grids in the IFS system. The second new method
involves the use of a novel algorithm for ocean and inland water separation.
This new algorithm may be used by anyone in the environmental modelling
community. To assess the impact of using these innovations, in situ
measurements of lake depth, lake water surface temperature and ice
formation/disappearance dates for 27 lakes collected by the Finnish
Environment Institute were used. A set of offline experiments driven by
atmospheric forcing from the ECMWF ERA5 Reanalysis were carried out using
the IFS HTESSEL land surface model. In terms of lake depth, the new dataset
shows a much lower mean absolute error, bias and error standard deviation
compared to the reference set-up. In terms of lake water surface
temperature, the mean absolute error is reduced by 13.4 %, the bias by
12.5 % and the error standard deviation by 20.3 %. Seasonal
verification of the mixed layer depth temperature and ice
formation/disappearance dates revealed a cold bias in the meteorological
forcing from ERA5. Spring, summer and autumn verification scores confirm an
overall reduction in the surface water temperature errors. For winter, no
statistically significant change in the ice formation/disappearance date
errors was detected.
15 citations
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European Centre for Medium-Range Weather Forecasts1, University of Reading2, National Oceanic and Atmospheric Administration3, George Mason University4, European Space Research and Technology Centre5, University of Lisbon6, National Center for Atmospheric Research7, Columbia University8, Met Office9, Goddard Space Flight Center10, Max Planck Society11, United States Naval Research Laboratory12, ETH Zurich13, University of Arizona14
TL;DR: This paper reviews the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling, and discusses recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly.
Abstract: In this paper, we review the use of satellite-based remote sensing in combination with in situ data to inform Earth surface modelling. This involves verification and optimization methods that can handle both random and systematic errors and result in effective model improvement for both surface monitoring and prediction applications. The reasons for diverse remote sensing data and products include (i) their complementary areal and temporal coverage, (ii) their diverse and covariant information content, and (iii) their ability to complement in situ observations, which are often sparse and only locally representative. To improve our understanding of the complex behavior of the Earth system at the surface and sub-surface, we need large volumes of data from high-resolution modelling and remote sensing, since the Earth surface exhibits a high degree of heterogeneity and discontinuities in space and time. The spatial and temporal variability of the biosphere, hydrosphere, cryosphere and anthroposphere calls for an increased use of Earth observation (EO) data attaining volumes previously considered prohibitive. We review data availability and discuss recent examples where satellite remote sensing is used to infer observable surface quantities directly or indirectly, with particular emphasis on key parameters necessary for weather and climate prediction. Coordinated high-resolution remote-sensing and modelling/assimilation capabilities for the Earth surface are required to support an international application-focused effort.
1 citations