Download Diapositiva 1

yes no Was this document useful for you?
   Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Document related concepts

IPCC Fourth Assessment Report wikipedia, lookup

Climate change, industry and society wikipedia, lookup

Surveys of scientists' views on climate change wikipedia, lookup

Effects of global warming on humans wikipedia, lookup

Public opinion on global warming wikipedia, lookup

Scientific opinion on climate change wikipedia, lookup

Climate change feedback wikipedia, lookup

Climatic Research Unit documents wikipedia, lookup

Solar radiation management wikipedia, lookup

Climate change and poverty wikipedia, lookup

Attribution of recent climate change wikipedia, lookup

Climate sensitivity wikipedia, lookup

Media coverage of global warming wikipedia, lookup

Climate change and agriculture wikipedia, lookup

Effects of global warming wikipedia, lookup

Global warming wikipedia, lookup

General circulation model wikipedia, lookup

Citizens' Climate Lobby wikipedia, lookup

Global warming hiatus wikipedia, lookup

Climate change in Tuvalu wikipedia, lookup

Instrumental temperature record wikipedia, lookup

Politics of global warming wikipedia, lookup

Climate engineering wikipedia, lookup

Climate governance wikipedia, lookup

Climate change adaptation wikipedia, lookup

Global warming controversy wikipedia, lookup

Economics of global warming wikipedia, lookup

Fred Singer wikipedia, lookup

Climate change denial wikipedia, lookup

Effects of global warming on human health wikipedia, lookup

Michael E. Mann wikipedia, lookup

Climatic Research Unit email controversy wikipedia, lookup

Soon and Baliunas controversy wikipedia, lookup

Climate resilience wikipedia, lookup

Heaven and Earth (book) wikipedia, lookup

ExxonMobil climate change controversy wikipedia, lookup

Years of Living Dangerously wikipedia, lookup

Urban heat island wikipedia, lookup

Regional and sub-regional climate simulations over Sub-Saharian African regions and the influence on the heat waves hazard
E. Bucchignani (1), P. Mercogliano (1), M. Montesarchio (1), P. Capuano (2) (3), M. Sellerino (2), A. Di Ruocco (2), and W. Kombe (4)
(1) C.M.C.C., Impacts on Soil and Coast, Italy ([email protected]), (2) AMRA S.c. a r.l., Napoli, Italy, (3) University of Salerno, Fisciano, Italy, (4) ARDHI University, Dar Es Salaam, Tanzania
1. Introduction
Africa is considered a continent particularly vulnerable to the effects of climate change but the
development of reliable predictions of future climate change is complicated by the lack of accurate baseline
data on current climate and the complexity of drivers of African climate variability (i.e., El Niño/Southern
Oscillation, land cover change). In the framework of the EU-FP7 CLUVA project (CLimate change and Urban
Vulnerability in Africa), regional projections of climate change at high spatial resolution, based on the IPCC
RCP4.5 and RCP8.5 scenarios have been performed for selected areas surrounding five African cities of
interest (i. e. the project test cities). Climate simulations, spanning over the period 1950-2050, have been
compared with large scale observed data (CRU) and with local data provided by the municipalities of the test
cities, in terms of two-metre temperature and precipitation. The variations of the fundamental climate
parameters will constrain different hazard at different time-scales. In the framework of the assessment of
the environmental impacts and the risks of climate changed induced hazards likely to affect urban areas at
various time frames, we analyzed a dataset of daily maximum temperature from Dar Es Salaam (Tanzania) in the
period 1961-2000 to evaluate the generation of heat waves, defined following a peak over threshold approach,
and the evolution in the extreme warm conditions up to 2050.
5. Heat Waves
3. Dar Es Salaam case
Fig. 3 shows the map of the bias of 2-meter temperature, (averaged over the time period 1971-2000) for DJF
(a) and JJA (b) obtained by comparing the CLM solution with CRU dataset. Fig. 4 shows the seasonal cycle of
temperature, respectively for CLM solution (with and without bias correction) and for observed data.
Fig. 4
Fig. 1
Fig. 2
Heat wave duration (Fig. 7b) is a key element to evaluate the
societal heat wave impacts, reducing the capacity of adaptation. The
maximum length (days) of heat wave episodes per year shows a mean
value of 4-5 days with some peak in 1974, 1987, 1994 and 2009.
Successively, there is a clear increase in the average duration of heat
wave with peaks of 18-19 days.
Fig. 7a,b
2. Regional and sub-regional climate simulations over Sub-Saharian African regions
A rectangular limited domain centred
over the city of Dar Es Salaam has been
considered (Fig.1). It has been discretized
using a spatial resolution of 8 km, employing
a computational grid with 95 x 135 grid
points and 40 vertical levels. The time
resolution is 50 sec. Fig. 2 shows the
Carbon Dioxide concentration time series
used to force the model, according to the
different scenarios.
Fig. 3
Climate projections are generally conducted using global models, which provide information around the whole
Earth surface. However, they are generally unsuitable to simulate the climate of very limited areas, since they
are characterized by resolutions generally around or coarser than 100 km, which is too poor for impact studies
on areas where many important phenomena occur at spatial scales lower than 10 km.
The usage of a Regional Climate Model (RCM) with a horizontal resolution of about 10 km can be a useful tool
for the description of the climate variability on local scale. The CLM is the climate version of the COSMO
model, which is the operational non-hydrostatic mesoscale weather forecast model developed by the German
Weather Service. Successively, the model has been updated by the CLM-Community, in order to develop also
climatic applications. A huge variety of applications of the model system exists, covering high resolution
simulations of mega-cities to medium resolution simulations of continents, tropical to arctic latitudes, paleo
studies, the recent past and climate scenarios for the 21st century. This makes it highly relevant for climate
science and for climate mitigation and adaptation politics.
For this specific activity, the utility of COSMO-CLM is due to the fact that
the model contains physical parameterizations (cloud, precipitations, moist
convection radiation) suitable for African areas. In the present work the climate
predictions have been obtained forcing COSMO-CLM with the CMCC-MED global
model output. The IPCC emission scenarios considered are the RCP4.5 and RCP8.5.
The number of hot days per year (Fig. 7a) shows an increase of
heat wave recurrence from 1961 to 1995. The successive period 19962000 shows a minimum in the recurrence of the phenomenon, but the
last period 2001-2011 has experienced a strong increase in the Heat
Wave occurrence. Hot days number evaluated by observed and modeled
data are also compared. The forecasted period 2011-2050 shows a
strong increase in the heat waves occurrence
Fig. 5 shows the time series of 2-meter temperature
averaged over a square area (length 40 km), centred on Dar
Es Salaam and obtained with both the considered scenarios.
An increase of temperature is projected, especially employing
RCP8.5 scenarios.
Heat wave duration and hot days number are strictly correlated
(Fig. 8) showing that the temperature rise could generate not only an
increase of heat waves number but mainly a longer average duration,
that can strongly affect the resilience capacity of the population,
particularly the elder people.
Fig. 5
Fig. 9
The frequency distribution plot of hot days duration (Fig. 9) for
five separate bi-decadal periods, according to the legend of top right
corner, shows the temporal change of heat wave characteristics. This
distribution have became longer tailed with time. The number of events
of the maximum length lasting 5 days could increase from 3 to 24 over
100 years (from 1950-70 to 2030-2050).
4. Heat Waves
Heat wave generally refers to periods of exceptionally warm temperatures, but there is no universal
technical definition (Robinson, 2001). The impacts of heat waves on the society are determined also by
temporal duration (Stephenson, 2008), in addition to their frequency, in fact the capacity of adaptation can
be reduced with prolonged exposure to high temperature and humidity.
A heat-wave is defined, here, as a period in which the maximum
temperatures are over the 90th percentile of the monthly distribution
for at least three days, fixing a minimum threshold for Tmax to
exclude months with less hot temperature (similar to winter time),
that for Dar Es Salaam was fixed to 32°C. The 90th percentile has
been evaluated over a climatological base period (1961-1990).
Fig. 6b
6. Conclusions
Fig. 6a
Fig. 6a shows the comparison between the observed and bias-corrected
modelled cumulative relative frequency distribution (CRFD) for daily Tmax.
The differences are partly due to an effect of the incompleteness of
observed dataset. Fig. 6b shows CRFD change over time, for each of ten
independent periods, according to the legend in the top right-hand corner.
Taking into account for the data coverage, it is clear that the Tmax peak
increases from 30.4°C (1961-70) to 31.5° (2001-2011). In addition all the
distribution is moving towards higher temperature in the last decades.
Regional Climate Model with a horizontal resolution lesser than 10x10 km can be a useful tool for the
description of the climate variability on local scale, suitable for impact studies. We evaluate regional climate
projection for some African areas, particularly for Dar Es Salaam. We use the climate simulations to evaluate
the changes in the heat wave occurrence. The combined change in the mean and variance of CRFD has
contributed to an increase in the frequency of hot days and in their duration. Projected future warming in the
Dar Es Salaam area shows a further increase in the heat waves parameters. The expected persistence of longlived heat waves lasting approximately 1.5-2 weeks is clearly longer with respect to the climatological period
(1961-1990). During 100 years, short lived but more intense waves are more than doubled in duration. It is
evident the needs for the national health services to develop strategies for the mitigation of the heat wave
effects, to enhance the resilience of the population, particularly the elder people.
Stephenson, D. B. (2008). Definition, diagnosis, and origin of extreme weather and climate events. In Diaz, H. and Murnane, R., editors, Climate extremes and society, chapter 1, pages 11–23.
Cambridge University Press;
Robinson P. J. (2001), On the Definition of a Heat Wave, J. Appl. Meteor., 40, 762-775;
Rockel B., Will A., Hense A., 2008. The regional Climate Model COSMO-CLM (CCLM), Meteorologische Zeitschrift, 17 (4): 347-348.