The Global Warming Potential (GWP) is used within the Kyoto Protocol to the United Nations Framework Convention on Climate Change as a metric for weighting the climatic impact of emissions of different greenhouse gases. The GWP has been subjected to many criticisms because of its formulation, but nevertheless it has retained some favour because of the simplicity of its design and application, and its transparency compared to proposed alternatives. Here, two new metrics are proposed, which are based on a simple analytical climate model. The first metric is called the Global Temperature Change Potential and represents the temperature change at a given time due to a pulse emission of a gas (GTP P ); the second is similar but represents the effect of a sustained emission change (hence GTP S ). Both GTP P and GTP S are presented as relative to the temperature change due to a similar emission change of a reference gas, here taken to be carbon dioxide. Both metrics are compared against an upwelling-diffusion energy balance model that resolves land and ocean and the hemispheres. The GTP P does not perform well, compared to the energy balance model, except for long-lived gases. By contrast, the GTP S is shown to perform well relative to the energy balance model, for gases with a wide variety of lifetimes. It is also shown that for time horizons in excess of about 100 years, the GTP S and GWP produce very similar results, indicating an alternative interpretation for the GWP. The GTP S retains the advantage of the GWP in terms of transparency, and the relatively small number of input parameters required for calculation. However, it has an enhanced relevance, as it is further down the cause-effect chain of the impacts of greenhouse gases emissions and has an unambiguous interpretation. It appears to be robust to key uncertainties and simplifications in its derivation and may be an attractive alternative to the GWP.
Climate data are used in a number of applications including climate risk management and adaptation to climate change. However, the availability of climate data, particularly throughout rural Africa, is very limited. Available weather stations are unevenly distributed and mainly located along main roads in cities and towns. This imposes severe limitations to the availability of climate information and services for the rural community where, arguably, these services are needed most. Weather station data also suffer from gaps in the time series. Satellite proxies, particularly satellite rainfall estimate, have been used as alternatives because of their availability even over remote parts of the world. However, satellite rainfall estimates also suffer from a number of critical shortcomings that include heterogeneous time series, short time period of observation, and poor accuracy particularly at higher temporal and spatial resolutions. An attempt is made here to alleviate these problems by combining station measurements with the complete spatial coverage of satellite rainfall estimates. Rain gauge observations are merged with a locally calibrated version of the TAMSAT satellite rainfall estimates to produce over 30-years (1983-todate) of rainfall estimates over Ethiopia at a spatial resolution of 10 km and a ten-daily time scale. This involves quality control of rain gauge data, generating locally calibrated version of the TAMSAT rainfall estimates, and combining these with rain gauge observations from national station network. The infrared-only satellite rainfall estimates produced using a relatively simple TAMSAT algorithm performed as good as or even better than other satellite rainfall products that use passive microwave inputs and more sophisticated algorithms. There is no substantial difference between the gridded-gauge and combined gauge-satellite products over the test area in Ethiopia having a dense station network; however, the combined product exhibits better quality over parts of the country where stations are sparsely distributed.
In northern and central Ethiopia, 2015 was a very dry year. Rainfall was only from one-half to three-quarters of the usual amount, with both the “belg” (February–May) and “kiremt” rains (June–September) affected. The timing of the rains that did fall was also erratic. Many crops failed, causing food shortages for many millions of people. The role of climate change in the probability of a drought like this is investigated, focusing on the large-scale precipitation deficit in February–September 2015 in northern and central Ethiopia. Using a gridded analysis that combines station data with satellite observations, it is estimated that the return period of this drought was more than 60 years (lower bound 95% confidence interval), with a most likely value of several hundred years. No trend is detected in the observations, but the large natural variability and short time series means large trends could go undetected in the observations. Two out of three large climate model ensembles that simulated rainfall reasonably well show no trend while the third shows an increased probability of drought. Taking the model spread into account the drought still cannot be clearly attributed to anthropogenic climate change, with the 95% confidence interval ranging from a probability decrease between preindustrial and today of a factor of 0.3 and an increase of a factor of 5 for a drought like this one or worse. A soil moisture dataset also shows a nonsignificant drying trend. According to ENSO correlations in the observations, the strong 2015 El Niño did increase the severity of the drought.
Sound climate risk management requires access to the best available decision-relevant climate information and the ability to use such information effectively. The availability and access of such information and the ability to use it is challenging, particularly throughout rural Africa. A gap analysis published by the International Research Institute for Climate and Society (IRI) and the Global Climate Observing System (GCOS) in 2005 explored these challenges in detail and identified four key gaps: (i) gaps in integration of climate into policy; (ii) gaps in integration of climate into practice at scale; (iii) gaps in climate services; and (iv) gaps in climate data. Though this document was published nearly nine years ago, the gaps it highlighted are still relevant today. In the last decade, IRI has been making efforts to address these critical issues in a systematic way through projects and partnerships in Africa. This paper describes IRI's efforts in Ethiopia, a country particularly prone to climate related risks. Here we outline a creative solution to bridge the gaps in the availability, access and use of national climate information through the Enhancing National Climate Services (ENACTS) initiative. We then discuss how policy and practice has changed as a result of IRI engagement in the development of climate services in the water, public health and agricultural sectors. The work in Ethiopia is indicative of the efforts IRI is implementing in other countries in Africa and in other parts of the world.
An attempt was made to investigate the sensitivity of water resources to climate change in the Awash River Basin in Ethiopia. The climate of the basin varies from humid subtropical to arid. The basin was divided into 3 subcatchments for better resolution in calibration and simulation. Stationbased meteorological data were processed to obtain areal averages necessary for the simulation. Different sets of temperature and rainfall scenarios were developed using GCM (both transient and CO 2 doubling) and incremental scenarios. The IIASA integrated water balance model (WatBal) was used to estimate runoff under a changed climate. The model represents the water balance among surface outflow, subsurface outflow, and evapotranspiration. The model was calibrated using a 10 yr period (1971 to 1980), validated with the next 6 yr period (1981 to 1986), and then applied for different climate scenarios. Results of the impact assessment over the basin showed a projected decrease in runoff, which ranged from -10 to -34%, with doubling of CO 2 and transient scenarios of CO 2 increase (GFD3, CCCM, GF01). Sensitivity analysis based on incremental scenarios showed that a drier and warmer climate change scenario results in reduced runoff.
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