学术活动

LASG学术报告(4-20)



Was this weather event caused by our emissions of greenhouse gases?

Dr. Chris Lennard
University of Cape Town

2010年4月20日上午10:00 科研楼101报告厅

Abstract
Regional climate change information is essential for the development
of adaptation strategies in an African context. Although this region
is one of the most vulnerable to the impacts of climate change, there
is very little regional scale information available to inform these
strategies. The Climate Systems Analysis Group (CSAG), based at the
University of Cape Town in South Africa, is one of a handful of
African groups contributing to filling in this knowledge gap.
Activities addressing this include seasonal forecasting, regional
downscaling (using numerical and statistical methods), CORDEX, global
modelling, generation of a wind atlas for South Africa, examination of
extreme events, climate change information dissemination and
detection/attribution studies. Some of these activities will be
presented and specifically the preliminary results from the
detection/attribution study (see below).
As everyone becomes increasingly aware and concerned about climate
change this question keeps being asked by taxpayers and those trying
to implement strategies to adapt to climate change. Unfortunately, the
climate change research community has focussed more on the past and
future rather than the present, and thus the popular attribution
questions have remained unanswered. Adaptation activities have had to
make do with products designed for informing mitigation activities.
Here we present the world's first real-time product to examine whether
and how human greenhouse gas emissions have contributed to our
weather. This service is produced in parallel with a monthly seasonal
forecast and will have been running in a test mode for half a year by
the time of this meeting. The motivation and goals will be discussed,
along with some preliminary insights gained from in-house discussions
and comments from colleagues. In particular, it is quite clear that
this service differs in crucial ways from a standard seasonal forecast
and thus must be treated accordingly.


Modes of variability of Southern Hemisphere atmospheric circulation
estimated by General Circulation Models
Dr. Simon Grainger
Bureau of Meteorology, Australia

2010年4月20日上午10:45 科研楼101报告厅
The seasonal mean variability of the atmospheric circulation is
affected by processes with time scales from less than seasonal to
interannual or longer. Using monthly mean data from General
Circulation Models (GCMs), the interannual variability of the seasonal
mean is separated into intraseasonal, and slowly varying components.
For Atmospheric GCM (AGCMs) ensembles, the slowly varying component is
further separated into internal and externally forced components. This
is done for Southern Hemisphere 500hPa geopotential height from five
C20C AGCM ensembles for the summer and winter seasons. In both
seasons, the intraseasonal and slow modes of variability are
qualitatively well reproduced by the models when compared with
reanalysis data, with a relative metric finding little overall
difference between the models. The Southern Annular Mode (SAM) is by
far the dominant mode of slowly varying internal atmospheric
variability. Two slow-external modes of variability are related to El
Nino-Southern Oscillation (ENSO) variability, and a third is the
atmospheric response to trends in external forcing. An ENSO-SAM
relationship is found in the model slow modes of variability, similar
to that found by earlier studies using reanalysis data. There is a
greater spread in the representation of model slow-external modes in
winter than summer, particularly in the atmospheric response to
forcing trends. Intraseasonal and slow modes of variability are also
estimated using realisations from the Coupled Model Intercomparison
Project Phase 3 (CMIP3) models for their twentieth century coupled
climate simulation (20c3m) experiment. The intraseasonal modes of
variability are generally well reproduced across all CMIP3 20c3m
models in both seasons. The slow modes are in general less well
reproduced than the intraseasonal modes. Differences between
realisations are generally less than inter-model differences, and
there is a greater spread of results for overall model diagnostics
than found for the C20C AGCMs.


The role of sea ice in the surface warming

Dr. Wanqiu Wang

Climate Prediction Center/NCEP


2010年4月20日上午11:30 科研楼101报告厅

Abstract

The observed land-surface temperature has shown an overall warming
during the past 100 years with the largest trend after 1980.
Accompanying the recent land surface warming is the increase in global
sea surface temperature (SST) and decrease in Arctic sea ice
concentration (SIC). Previous studies demonstrated that the observed
global warming can be reproduced by coupled atmosphere-ocean models
with observed evolving external forcing and by atmosphere-only general
circulation models with observed oceanic surface conditions. In this
study, we analyze the role of sea ice in the long-term trend of land
surface air temperatures with a focus on the spatial distribution of
temperature changes. The analysis is based on a suite of simulations
with the National Centers for Environmental Prediction (NCEP)
atmospheric Global Forecast System (GFS) model. Simulations were
carried out with average seasonal sea surface conditions of two
five-year periods: 1982-1986 and 2003-2007. For each period, the ocean
surface is specified with anomalies of the SIC only, the SST only, and
both the SIC and SST. We will address the following questions with
these simulations: (1) what are the relative contributions of the SIC
and SST to the warming of the land surface, (2) how the impacts are
manifested spatially, and (3) to what extent the impacts of the SIC
and SST are linearly additive? We will also present an analysis of the
role of the SIC in the climate anomalies in 2007 during which the
Arctic sea ice reached its lowest extent since 1978. The impact of the
SIC on Arctic surface temperature and its seasonal and spatial
variations will be examined based on 150 runs with three
atmosphere-only general circulation models.















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