Climate Downscaling over Nordeste, Brazil, Using the NCEP RSM97

Sun, Liqiang; Moncunill, David Ferran; Li, Huilan; Moura, Antônio Divino; Filho, Francisco de Assis de Souza

doi:10.1175/jcli-3266.1

Cited by 59 publications

(35 citation statements)

References 42 publications

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“…Souza Filho & Lall 2003, Sun et al 2005, 2007 based on a complete arsenal of regional climatic statistical and numerical modelling (using various types of downscaling techniques). In collaboration with other national and international institutes (e.g.…”

Section: Discussionmentioning

confidence: 99%

Relationship between ocean climatic variability and rain-fed agriculture in northeast Brazil

Alves¹,

Servain²,

Campos³

2009

Clim. Res.

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The statistical relationship between sea-surface temperature variability over tropical oceans and the 1952 to 2000 crops of maize and beans in the state of Ceará, Brazil, was investigated. This region mainly receives precipitation from February to May. The climatic indices used were the Niño-3 for the Pacific and the dipole index for the Atlantic. In a first series of diagnostic analyses, the climatic precursor variables were examined from November to January (Years 0 to 1) for the Niño-3 index and from February to April (Year 1) for the Atlantic dipole components. The agronomic variables were the planted area, annual production, yield (which showed a negative linear trend during the study period), price, and aggregate value (price/yield) for Year 1. A more robust statistical weight was obtained with the Atlantic dipole. The Pacific weight added to, or decreased, the Atlantic effect, depending on the sign of the index. Ocean climatic conditions that generate drought episodes in the Nordeste region of Brazil are generally associated with bad harvests. However, the reverse (i.e. wet weather episodes) does not necessarily produce good harvests. In a second series of prognostic analyses, a statistical model used the ocean climatic information available before the rainy season to forecast the yields of maize and beans. The model gave some relevant data in terms of yield tendencies, and was more sensitive when the climatic conditions were neutral or dry. Remarkably good yield forecasts were noted at the end of the study period. Harvests depend on complex combinations of multiple climatic criteria related, not only to quantitative, but also qualitative factors connected with the rainy season.KEY WORDS: Climate variability · Northeast Brazil ('Nordeste') · Climatic impact · Rain-fed agriculture Resale or republication not permitted without written consent of the publisher

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Section: Discussionmentioning

confidence: 99%

Relationship between ocean climatic variability and rain-fed agriculture in northeast Brazil

Alves¹,

Servain²,

Campos³

2009

Clim. Res.

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“…In this approach, the regional climate model is forced by low-resolution fields from the AGCM. This method has been used in many problems, including seasonal precipitation forecasts over the northeast of Brazil (Sun et al, 2005(Sun et al, , 2006. The purpose of this work is to investigate whether the higher resolution of the regional model could improve upon the skill of the AGCM to simulate seasonal TC activity.…”

Section: Introductionmentioning

confidence: 99%

Feasibility study for downscaling seasonal tropical cyclone activity using the NCEP regional spectral model

Camargo

Sun

2006

Intl Journal of Climatology

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Abstract:The potential use of the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) for downscaling seasonal tropical cyclone (TC) activity was analyzed here. The NCEP RSM with horizontal resolution of 50 km, was used to downscale the ECHAM4.5 Atmospheric General Circulation Model (AGCM) simulations forced with observed sea surface temperature (SST) over the western North Pacific. An ensemble of ten runs for June-November 1994 and 1998 was studied. The representation of the TCs is much improved compared to the low-resolution forcing AGCM, but the TCs are not as intense as observed ones, as the RSM horizontal resolution is not sufficiently high. The large-scale fields of the RSM are examined and compared to both the AGCM and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis. The large-scale fields of RSM characteristics are in general similar to those of the reanalysis. Various properties of the TCs in the RSM are also examined such as first positions, tracks, accumulated cyclone energy (ACE) and duration. While the RSM does not reproduce the higher number of TCs in 1994 than in 1998, other measures of TC activity (ACE, number of cyclone days) in the RSM are higher in 1994 than in 1998.

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“…An example would be when a grid square is located mainly over the windward part of an mountain range, but we are interested in forecasting for a town on the inland, or leeward, side of the mountain range-a location whose rainfall anomaly tendency may be in the opposite sense to that of the average over the grid square. Resolving the often opposing anomalies may be done either statistically, as tested in South Africa (Landman et al 2009), or through use of a regional dynamical model, as was done for semi-arid northeast Brazil in an IRI project in the early 2000s (Sun et al 2005), or likewise in recent work focusing both there and in Chile, in the context of water management issues .…”

Section: Specialized or Tailored Forecastsmentioning

confidence: 99%

Climate information, outlooks, and understanding–where does the IRI stand?

Barnston

Tippett

2014

Earth Perspect

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The International Research Institute for Climate and Society (IRI) began providing user-oriented climate information, including outlooks, in the late 1990s. Its climate products are intended to meet the needs of decision makers in various sectors of society such as agriculture, water management, health, disaster management, energy, education and others. They try to link the current state of the science in climate diagnostics and prediction to the dynamically evolving practical needs of users worldwide. Because most users are not climate scientists, the manner in which the information is provided is of paramount importance in order for it to be understandable and actionable. Non-technical language that preserves essential content is required, as well as graphics that are intuitive and largely self-explanatory. The climate information products themselves must be in demand by users, rather than ones that the producers believe would be best. These requirements are consistent with IRI's mission of improving human welfare, particularly in developing countries where decision makers may not initially know what climate information they need, and how best to use it. This lack of initial understanding requires back-and-forth communication between the producers and users to initiate and sustain uptake and beneficial use of the information. Backed by its climate prediction research, the IRI's climate information products span time-scales of days to decades. Experience on the statistics of daily weather behavior within seasons has been gleaned, as has the benefits of statistical and dynamical spatial downscaling of predictions. By providing views in a progressive sequence of temporal scales, IRI's products help demonstrate that preparation for interannual climate variability may be the best preparation for decadal variability and trends related to climate change.

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Climate Downscaling over Nordeste, Brazil, Using the NCEP RSM97

Cited by 59 publications

References 42 publications

Relationship between ocean climatic variability and rain-fed agriculture in northeast Brazil

Relationship between ocean climatic variability and rain-fed agriculture in northeast Brazil

Feasibility study for downscaling seasonal tropical cyclone activity using the NCEP regional spectral model

Climate information, outlooks, and understanding–where does the IRI stand?

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