scholarly journals What climate change means for farmers in Africa: A triptych review Left panel: Increasing climate variability and a response approach for African farmers

2014 ◽  
Vol 14 (61) ◽  
pp. 8445-8458
Author(s):  
CJ Stigter ◽  
◽  
E Ofori ◽  
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Climate Variability ◽  
Climate Models ◽  
Southern Oscillation ◽  
Climate Science ◽  
Direct Consequence ◽  
Sub Saharan Africa ◽  
Local Conditions ◽  
Weather And Climate

In this paper in three parts, climate change is approached by dealing with the three sides from which the danger comes: (i) global warming, (ii) increasing climate variability, (iii) more (and possibly more severe) meteorological and climatological extreme events. These are the three panels of this triptych review and this left panel is about (ii). This second panel starts with a compelling review of the present situation of food security, referring to African examples to improve the situation. Then the influence is discussed that the El Niño Southern Oscillation (ENSO) has on increasing climate variability as a consequence of climate change. It is indicated that, to date, climate models have been developed with little knowledge of agricultural systems dynamics. On the other hand one can illustrate that agricultural policy analysis has been conducted with little knowledge of climate dynamics. As a direct consequence of capricious behaviour of particularly rainfall in West Africa, the adaptation of its farmers has lagged behind enormously. This statement is valid for most farmers in sub-Saharan Africa. Within the climate science community there is an emerging effort to make findings more suitable for decision making, but as yet there is little consensus as to how data may be relied upon for decision making. Then a lot of attention is paid to how response farming, that is thoroughly defined, can play an important role in coping with the consequences of climate variability. Response farming is often limited envisaging rainfall events, but coping with weather and climate (and often soil) disasters as well as using windows of weather and climate (and often soil) opportunities are other forms of responding to weather and climate (and often soil) realities. Services such as in advice on design rules on above and below ground microclimate management or manipulation, with respect to any appreciable microclimatic improvement: shading, wind protection, mulching, other surface modification, drying, storage, frost protection and so on belong to such “response farming” agrometeorological services. Ideally, to get optimal preparations, farmers get advisories/services through extension intermediaries, backed by scientists, to properly understand decision options through discussions supported by economic analyses. Throughout the paper text boxes are used that illustrate local conditions that must be taken into account if one wants to understand the impacts/consequences of climate change for African farmers and how they may cope with them.

VARIABILIDAD CLIMÁTICA, CAMBIO CLIMÁTICO Y PESQUERÍAS EN EL ECUADOR.

2012 ◽  
Vol 1 (1) ◽  
Author(s):  
Johnny Chavarría Viteri ◽  
Dennis Tomalá Solano
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Pacific Decadal Oscillation ◽  
El Niño ◽  
El Nino ◽  
Southern Oscillation ◽  
El Niño Southern Oscillation ◽  
El Nino Southern Oscillation ◽  
Current Action ◽  
Palabras Clave

La variabilidad climática es la norma que ha modulado la vida en el planeta. Este trabajo demuestra que las pesquerías y acuicultura costera ecuatorianas no son la excepción, puesto que tales actividades están fuertemente influenciadas por la variabilidad ENSO (El Niño-Oscilación del Sur) y PDO (Oscilación Decadal del Pacífico), planteándose que la señal del cambio climático debe contribuir a esta influencia. Se destaca también que, en el análisis de los efectos de la variabilidad climática sobre los recursos pesqueros, el esfuerzo extractivo también debe ser considerado. Por su parte, la acción actual de la PDO está afectando la señal del cambio climático, encontrándose actualmente en fases opuestas. Se espera que estas señales entren en fase a finales de esta década, y principalmente durante la década de los 20 y consecuentemente se evidencien con mayor fuerza los efectos del Cambio Climático. Palabras Clave: Variabilidad Climática, Cambio Climático, ENSO, PDO, Pesquerías, Ecuador. ABSTRACT Climate variability is the standard that has modulated life in the planet. This work shows that the Ecuadorian  fisheries and aquaculture are not the exception, since such activities are strongly influenced by ENSO variability (El Niño - Southern Oscillation) and PDO (Pacific Decadal Oscillation), considering that the signal of climate change should contribute to this influence. It also emphasizes that in the analysis of the effects of climate variability on the fishing resources, the extractive effort must also be considered. For its part, the current action of the PDO is affecting the signal of climate change, now found on opposite phases. It is hoped that these signals come into phase at the end of this decade, and especially during the decade of the 20’s and more strongly evidencing the effects of climate change. Keywords: Climate variability, climate change, ENSO (El Niño - Southern Oscillation) and PDO  (Pacific Decadal Oscillation); fisheries, Ecuador. Recibido: mayo, 2012Aprobado: agosto, 2012

scholarly journals Ubiquity of human-induced changes in climate variability

2021 ◽  
Vol 12 (4) ◽  
pp. 1393-1411
Author(s):  
Keith B. Rodgers ◽  
Sun-Seon Lee ◽  
Nan Rosenbloom ◽  
Axel Timmermann ◽  
Gokhan Danabasoglu ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Climate Adaptation ◽  
Southern Oscillation ◽  
System Modeling ◽  
Greenhouse Warming ◽  
Earth System ◽  
Wide Range ◽  
State Changes ◽  
Mean State

Abstract. While climate change mitigation targets necessarily concern maximum mean state changes, understanding impacts and developing adaptation strategies will be largely contingent on how climate variability responds to increasing anthropogenic perturbations. Thus far Earth system modeling efforts have primarily focused on projected mean state changes and the sensitivity of specific modes of climate variability, such as the El Niño–Southern Oscillation. However, our knowledge of forced changes in the overall spectrum of climate variability and higher-order statistics is relatively limited. Here we present a new 100-member large ensemble of climate change projections conducted with the Community Earth System Model version 2 over 1850–2100 to examine the sensitivity of internal climate fluctuations to greenhouse warming. Our unprecedented simulations reveal that changes in variability, considered broadly in terms of probability distribution, amplitude, frequency, phasing, and patterns, are ubiquitous and span a wide range of physical and ecosystem variables across many spatial and temporal scales. Greenhouse warming in the model alters variance spectra of Earth system variables that are characterized by non-Gaussian probability distributions, such as rainfall, primary production, or fire occurrence. Our modeling results have important implications for climate adaptation efforts, resource management, seasonal predictions, and assessing potential stressors for terrestrial and marine ecosystems.

scholarly journals Land surface phenological response to decadal climate variability across Australia using satellite remote sensing

2014 ◽  
Vol 11 (5) ◽  
pp. 7685-7719 ◽  
Author(s):  
M. Broich ◽  
A. Huete ◽  
M. G. Tulbure ◽  
X. Ma ◽  
Q. Xin ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Land Management ◽  
Land Surface ◽  
Vegetation Index ◽  
Southern Oscillation ◽  
Quantitative Information ◽  
Decadal Climate Variability ◽  
Eastern Australia ◽  
Phenological Cycle

Abstract. Land surface phenological cycles of vegetation greening and browning are influenced by variability in climatic forcing. Quantitative information on phenological cycles and their variability is important for agricultural applications, wildfire fuel accumulation, land management, land surface modeling, and climate change studies. Most phenology studies have focused on temperature-driven Northern Hemisphere systems, where phenology shows annually reoccurring patterns. Yet, precipitation-driven non-annual phenology of arid and semi-arid systems (i.e. drylands) received much less attention, despite the fact that they cover more than 30% of the global land surface. Here we focused on Australia, the driest inhabited continent with one of the most variable rainfall climates in the world and vast areas of dryland systems. Detailed and internally consistent studies investigating phenological cycles and their response to climate variability across the entire continent designed specifically for Australian dryland conditions are missing. To fill this knowledge gap and to advance phenological research, we used existing methods more effectively to study geographic and climate-driven variability in phenology over Australia. We linked derived phenological metrics with rainfall and the Southern Oscillation Index (SOI). We based our analysis on Enhanced Vegetation Index (EVI) data from the MODerate Resolution Imaging Spectroradiometer (MODIS) from 2000 to 2013, which included extreme drought and wet years. We conducted a continent-wide investigation of the link between phenology and climate variability and a more detailed investigation over the Murray–Darling Basin (MDB), the primary agricultural area and largest river catchment of Australia. Results showed high inter- and intra-annual variability in phenological cycles. Phenological cycle peaks occurred not only during the austral summer but at any time of the year, and their timing varied by more than a month in the interior of the continent. The phenological cycle peak magnitude and integrated greenness were most significantly correlated with monthly SOI within the preceding 12 months. Correlation patterns occurred primarily over north-eastern Australia and within the MDB predominantly over natural land cover and particularly in floodplain and wetland areas. Integrated greenness of the phenological cycles (surrogate of productivity) showed positive anomalies of more than two standard deviations over most of eastern Australia in 2009–2010, which coincided with the transition between the El Niño induced decadal droughts to flooding caused by La Niña. The quantified spatial-temporal variability in phenology across Australia in response to climate variability presented here provides important information for land management and climate change studies and applications.

scholarly journals Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability

2020 ◽  
Vol 24 (6) ◽  
pp. 3251-3269 ◽  
Author(s):  
Chao Gao ◽  
Martijn J. Booij ◽  
Yue-Ping Xu
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
River Basin ◽  
Climate Models ◽  
Global Climate ◽  
Global Climate Models ◽  
Internal Climate Variability ◽  
Low Flows ◽  
Uncertainty Sources ◽  
High Flows

Abstract. Projections of streamflow, particularly of extreme flows under climate change, are essential for future water resources management and the development of adaptation strategies to floods and droughts. However, these projections are subject to uncertainties originating from different sources. In this study, we explored the possible changes in future streamflow, particularly for high and low flows, under climate change in the Qu River basin, eastern China. ANOVA (analysis of variance) was employed to quantify the contribution of different uncertainty sources from RCPs (representative concentration pathways), GCMs (global climate models) and internal climate variability, using an ensemble of 4 RCP scenarios, 9 GCMs and 1000 simulated realizations of each model–scenario combination by SDRM-MCREM (a stochastic daily rainfall model coupling a Markov chain model with a rainfall event model). The results show that annual mean flow and high flows are projected to increase and that low flows will probably decrease in 2041–2070 (2050s) and 2071–2100 (2080s) relative to the historical period of 1971–2000, suggesting a higher risk of floods and droughts in the future in the Qu River basin, especially for the late 21st century. Uncertainty in mean flows is mostly attributed to GCM uncertainty. For high flows and low flows, internal climate variability and GCM uncertainty are two major uncertainty sources for the 2050s and 2080s, while for the 2080s, the effect of RCP uncertainty becomes more pronounced, particularly for low flows. The findings in this study can help water managers to become more knowledgeable about and get a better understanding of streamflow projections and support decision making regarding adaptations to a changing climate under uncertainty in the Qu River basin.

scholarly journals Farmers’ Perceptions on Climate Variability and Adaptation Strategies to Climate Change in Cinzana, Mali

2016 ◽  
Vol 4 (3) ◽  
pp. 13 ◽  
Author(s):  
Touré Halimatou ◽  
Zampaligre Nouhoun ◽  
Traoré Kalifa ◽  
Kyei-Baffour Nicholas
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Meteorological Data ◽  
Rainfall Variability ◽  
Adaptation Strategies ◽  
Sub Saharan Africa ◽  
Farm Size ◽  
Annual Rainfall ◽  
Sub Saharan ◽  
Future Economic Development

Several studies predict that climate change will highly affect the African continent. These changes in climate and climate variability may be challenging issues for future economic development of the continent in general, and particularly in the region of sub Saharan Africa. Offering a case study of Sahelian zone of Mali in the present study aimed to understand farmers’ perceptions of climate variability and change and to evaluate adaptation options used by farmers in the Cinzana commune of Mali. One hundred and nineteen farmers were interviewed using a questionnaire designed with six sections. The result showed that all farmers interviewed were aware of climate change and climate variability. The Farmers perceived a decrease in annual rainfall variability and an increase of temperature as main factors of climate change and climate variability. The observed meteorological data, showed a decrease of precipitation distribution during the last 14 years of which was observed by farmers. Several strategies such as selling animals, use of improved crop varieties, new activities (outside agriculture) and credit were the commonly preferred adaptation strategies to deal with climate change and variability. Factors surveyed, age, gender, education, household size, farm size were found to be significantly correlated to self-reported to adaptation.

scholarly journals Impact of climate change on crop suitability in sub-Saharan Africa in parameterized and convection-permitting regional climate models

2020 ◽  
Vol 15 (9) ◽  
pp. 094086
Author(s):  
Sarah Chapman ◽  
Cathryn E Birch ◽  
Edward Pope ◽  
Susannah Sallu ◽  
Catherine Bradshaw ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Regional Climate ◽  
Regional Climate Models ◽  
Sub Saharan Africa ◽  
Impact Of Climate Change ◽  
Crop Suitability ◽  
Sub Saharan

scholarly journals The Benefits of Global High Resolution for Climate Simulation: Process Understanding and the Enabling of Stakeholder Decisions at the Regional Scale

2018 ◽  
Vol 99 (11) ◽  
pp. 2341-2359 ◽  
Author(s):  
M. J. Roberts ◽  
P. L. Vidale ◽  
C. Senior ◽  
H. T. Hewitt ◽  
C. Bates ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Time Scales ◽  
Climate Models ◽  
Water Cycle ◽  
Regional Scale ◽  
Coupled Model ◽  
Convective Precipitation ◽  
Climate Simulation ◽  
Intergovernmental Panel

AbstractThe time scales of the Paris Climate Agreement indicate urgent action is required on climate policies over the next few decades, in order to avoid the worst risks posed by climate change. On these relatively short time scales the combined effect of climate variability and change are both key drivers of extreme events, with decadal time scales also important for infrastructure planning. Hence, in order to assess climate risk on such time scales, we require climate models to be able to represent key aspects of both internally driven climate variability and the response to changing forcings. In this paper we argue that we now have the modeling capability to address these requirements—specifically with global models having horizontal resolutions considerably enhanced from those typically used in previous Intergovernmental Panel on Climate Change (IPCC) and Coupled Model Intercomparison Project (CMIP) exercises. The improved representation of weather and climate processes in such models underpins our enhanced confidence in predictions and projections, as well as providing improved forcing to regional models, which are better able to represent local-scale extremes (such as convective precipitation). We choose the global water cycle as an illustrative example because it is governed by a chain of processes for which there is growing evidence of the benefits of higher resolution. At the same time it comprises key processes involved in many of the expected future climate extremes (e.g., flooding, drought, tropical and midlatitude storms).

scholarly journals Cognitive Biases about Climate Variability in Smallholder Farming Systems in Zambia

2019 ◽  
Vol 11 (2) ◽  
pp. 369-383 ◽  
Author(s):  
Kurt B. Waldman ◽  
Noemi Vergopolan ◽  
Shahzeen Z. Attari ◽  
Justin Sheffield ◽  
Lyndon D. Estes ◽  
...  
Keyword(s):  
Climate Change ◽  
Climate Variability ◽  
Land Surface ◽  
Rainy Season ◽  
Environmental Data ◽  
Sub Saharan Africa ◽  
Future Climate Change ◽  
Surface Model ◽  
Smallholder Farming ◽  
Farmer Perceptions

Abstract Given the varying manifestations of climate change over time and the influence of climate perceptions on adaptation, it is important to understand whether farmer perceptions match patterns of environmental change from observational data. We use a combination of social and environmental data to understand farmer perceptions related to rainy season onset. Household surveys were conducted with 1171 farmers across Zambia at the end of the 2015/16 growing season eliciting their perceptions of historic changes in rainy season onset and their heuristics about when rain onset occurs. We compare farmers’ perceptions with satellite-gauge-derived rainfall data from the Climate Hazards Group Infrared Precipitation with Station dataset and hyper-resolution soil moisture estimates from the HydroBlocks land surface model. We find evidence of a cognitive bias, where farmers perceive the rains to be arriving later, although the physical data do not wholly support this. We also find that farmers’ heuristics about rainy season onset influence maize planting dates, a key determinant of maize yield and food security in sub-Saharan Africa. Our findings suggest that policy makers should focus more on current climate variability than future climate change.

scholarly journals Atmospheric Blocking and Mean Biases in Climate Models

2010 ◽  
Vol 23 (23) ◽  
pp. 6143-6152 ◽  
Author(s):  
Adam A. Scaife ◽  
Tim Woollings ◽  
Jeff Knight ◽  
Gill Martin ◽  
Tim Hinton
Keyword(s):  
Climate Change ◽  
Climate Models ◽  
Climate Model ◽  
Horizontal Resolution ◽  
Small Scale ◽  
Model Data ◽  
Atmospheric Blocking ◽  
Intergovernmental Panel ◽  
Weather And Climate ◽  
Main Culprit

Abstract Models often underestimate blocking in the Atlantic and Pacific basins and this can lead to errors in both weather and climate predictions. Horizontal resolution is often cited as the main culprit for blocking errors due to poorly resolved small-scale variability, the upscale effects of which help to maintain blocks. Although these processes are important for blocking, the authors show that much of the blocking error diagnosed using common methods of analysis and current climate models is directly attributable to the climatological bias of the model. This explains a large proportion of diagnosed blocking error in models used in the recent Intergovernmental Panel for Climate Change report. Furthermore, greatly improved statistics are obtained by diagnosing blocking using climate model data corrected to account for mean model biases. To the extent that mean biases may be corrected in low-resolution models, this suggests that such models may be able to generate greatly improved levels of atmospheric blocking.

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