Bulletin

wall bulletin
Overview

Authors: 超级管理员 | Edit: zhangxin

Chapter 1 describes the CropWatch agroclimatic indicatorsf or rainfall (RAIN), temperature (TEMP), and radiation (RADPAR), along with the agronomic indicator for potential biomass (BIOMSS) for sixty-five global Monitoring and Reporting Units (MRU). Rainfall, temperature, and radiation indicators arecompared to their average value for the same period over the last fourteen years (called the “average”), while BIOMSS is compared to the indicator’s average of the recent five years. Indicator values for all MRUs are included in Annex A, table A.1. For more information about the MRUs and indicators, please see Annex C and online CropWatch resources at www.cropwatch.com.cn.

1.1 Overview

Global patterns of rainfall for the four-month monitoring period of July to October 2015 are largely compatible with El Niño impacts, with several marked and regionally well-defined anomalies, especially in Central and South America,east and southern Africa as well as Southeast Asia and Oceania.

However, as is increasingly noted by the climate monitoring community,there is no such thing as a “normal” El Niño, nor is there any typical and predictable behaviour when moving beyond very broad regional patterns. El Niño means havoc,i.e. droughts, floods and cyclones, but the details change every time. For instance, the classical pattern would mean wet conditions in the Horn of Africa and east Africa, and drought in the south, as happened in 1991-1992.

During the current season, we have dry conditions over all of south and east Africa for the reporting period, with more severe impacts in the east as the CropWatch RAIN index shows a deficit of 19% in southern Africa (Mapping and Reporting Unit, MRU-09), and as much as 25% and 28% in the Horn of Africa (MRU-04)and the eastern African Highlands (MRU-02), respectively. Even equatorial central Africa (MRU-01) is affected (-6%) and north and central Madagascar (MRU-05)recorded above average RAIN (+12%).

Throughout the Caribbean, Central and South America suffered from drought (with deficits between 20% and 30%), except the east, from the Brazilian Nordeste (MRU-22) to the Argentinian Pampas (MRU-26), where the excess reached 53% in central-eastern Brazil (MRU-23).

As described in subsequent sections of this chapter, there are also spatially coherent patterns of abnormal rainfall in western, central and eastern Asia. They are, however, not part of the “normal” El Niño impacts,which stresses again that the currently on-going El Niño is rather atypical,except maybe in Oceania.

An unusually weak association between the climate variables also characterizes the current reporting period. For instance, there is no discernible link between rainfall and temperature anomalies, at the global oreven regional scales, including latitudinal variations. The loose association between RAIN and the CropWatch radiation indicator (RADPAR) is negative duringthe reporting period, as expected, but not statistically significant.

With the exception of Subarctic America (MRU-65) of which only the extreme south holds some agricultural relevance, all other extremes do notfollow any pattern (table 1.1).

 

 Table 1.1. RAIN, TEMP and RADPAR anomalies in the global MRU with the largest rain anomalies


MRU

RAIN (%)

TEMP (°C)

RADPAR (%)

56. New Zealand

-73

-0.5

-4

53. Northern Australia

-71

-0.7

5

65. Subarctic America

165

1.9

-8

32. Gansu-Xinjiang (China)

173

-0.4

0

47. Mongolia region

309

-0.1

0

 

Not all impacts of El Niño will be negative. For instance, the aforementioned rainfall anomaly of -6% in equatorial central Africa (MRU-01) is accompanied bya drop in temperature (-0.7°C) and an increase in sunshine (+6%), which corresponds to favourable conditions: The areas have abundant rainfall largely exceeding crop water requirements, low sunshine is usually limiting crop growth and night-time respiration loss is significant due to high temperature.Similar situations can only be identified on a case-by-case basis due to the absence of coherent spatial patterns among the climate variables.