Shifting patterns of mild weather in response to projected radiative forcing
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Climate change has been shown to impact the mean climate state and climate extremes. Though climate extremes have the potential to disrupt society, extreme conditions are rare by definition. In contrast, mild weather occurs frequently and many human activities are built around it. We provide a global analysis of mild weather based on simple criteria and explore changes in response to radiative forcing. We find a slight global mean decrease in the annual number of mild days projected both in the near future (−4 days per year, 2016–2035) and at the end of this century (−10 days per year, 2081–2100). Projected seasonal and regional redistributions of mild days are substantially greater. These changes are larger than the interannual variability of mild weather caused by El Niño–Southern Oscillation. Finally, we show an observed global decrease in the recent past, and that observed regional changes in mild weather resemble projections.
KeywordsProjected Change Climate Extreme CMIP5 Model Dewpoint Temperature Reanalysis Product
A large body of climate research is devoted to the changing character of the mean climate state (e.g., global mean temperature, sea level, sea ice extent, IPCC 2013) or climate extremes (e.g., heat waves, storms, heavy precipitation, droughts, IPCC 2012). Though these aspects are of large societal importance, the focus on the climatic mean state or climate extremes neglects the study of meteorological conditions that occur more regularly and are of societal significance in a different way.
The aim of this study is to investigate mild weather. Mild weather is weather that is neither too hot, too cold, too humid nor rainy–weather that could also be described as being “pleasant”. Mild weather occurs frequently in most parts of the world. It does not disrupt society the way climate extremes do, instead many human outdoor activities are enhanced by or depend on mild weather. Examples of such activities include picnics, football games, dog walks, bike rides, and outdoor events such as music festivals or weddings. Furthermore, the absence of mild weather during construction work, infrastructure projects, road works, landscaping projects, air travel, and rail or road transportation may cause delays with significant negative economic consequences. This relationship with recreational and industrial human activity makes mild weather a relevant meteorological condition for society.
We present a global study of the daily occurrence of mild weather based on simple and relatable criteria. The spatial and temporal distribution is discussed, and projections of change in response to projected radiative forcing in the near and far future are made. A high-resolution global climate model, HiFLOR (Murakami et al. 2015), is used to allow for the investigation of local features and changes.
Daily maximum air temperature between 18 and 30 °C.
Daily total precipitation not exceeding 1 mm.
Daily mean dewpoint temperature not exceeding 20 °C.
Temperatures are taken at 2 m reference height. These simple criteria may be refined for specific activities or regions in future studies. Lowering the precipitation criterion increases the uncertainty of the presented results due to disagreements between the model and reanalysis products. Very low precipitation intensities are notoriously difficult to model, leading to biases in both model data and reanalysis output (Dai 2006; Schmidli et al. 2006). Dewpoint temperature was chosen rather than the more familiar relative humidity because it is a better indicator of human comfort (Lawrence 2005). In the supplementary information, we show that our results are robust to reasonable changes to these criteria and provide an assessment of model bias and its impact on the results. The selection of mild weather has purposely been based on the daily occurrence of mild weather rather than the mildness of the mean climate. This is an important distinction as the value of the annual mean climate masks daily and seasonal variability in weather and climate, the variation that people relate to (Weber 2010).
The analysis has been based on model experiments performed with the NOAA GFDL HiFLOR global coupled model. HiFLOR is built from a high-resolution atmospheric/land model (0.25° × 0.25°) coupled to a low-resolution oceanic/sea ice model (1° × 1°, Murakami et al. 2015). The high atmospheric resolution allows for the better resolvement of coastlines and finer-scale mountains (e.g., Californian Sierra, European Alps) that are absent at resolution lower than 30 km (Kapnick and Delworth 2013; Van der Wiel et al. 2016).
A fourth experiment with interannually varying prescribed SSTs was performed to analyze El Niño-Southern Oscillation (ENSO) forced variability. In this historical experiment, SSTs were restored to the time varying 1971–2015 HadISST1.1 field. Six ensemble members were created to decrease the effects of atmospheric internal variability.
To test the robustness of the model results, the projected changes of mild weather occurrence from HiFLOR was compared to the change in the original CMIP5 data2. Note that the CMIP5 experiments are slightly different from the experiments with HiFLOR. SSTs in the CMIP5 experiments are not restored to a repeating climatology but are integrated in transient mode. To quantify biases in the distribution of mild weather in HiFLOR, model data were compared to the MERRA-2 reanalysis from 1980–2015 (Koster et al. 2015). Furthermore, a linearized climate change experiment was conducted using MERRA-2 daily data and monthly climate change estimates for daily maximum temperature, precipitation, and dewpoint from HiFLOR.
Human population-weighting was done based on data from CIESIN (2015) for the year 2000 (Suppl. Fig. 1). The year 2000 was chosen because it corresponds to the years of the control experiment. For simplicity and because of uncertainties in projections of human population change, all human population-based calculations are relative to the global population of 2000. Therefore, changes in the number of days with mild weather due to radiative forcing are given for a constant human population distribution (referred to as days per year per person).
Finally, the multivariate ENSO index (Wolter and Timlin 2011) is used, this is an ENSO index based on six observed atmospheric and oceanic variables (sea-level pressure, zonal and meridional surface wind, sea surface temperature, surface air temperature, and total cloud fraction). Because ENSO is a coupled atmosphere-ocean phenomenon, this index is preferable over an index purely based on sea surface temperatures. MEI data was downloaded from the NOAA ESRL website3.
3.1 Present day global distribution of mild weather
There is large seasonality in the distribution of mild weather in the temperate regions, for selected locations, this is shown in Fig. 1b–g (additional locations are included in Suppl. Fig. 4). Close to the tropics, summers are too humid (i.e., humidity-limited) and mild weather is limited to winter (Fig. 1d, e). The mid-latitudes are cold-limited in winter and mild weather occurs primarily during summer (Fig. 1c, f). Mediterranean climates experience two seasons of mild weather annually: summers are heat-limited (Fig. 1b, g), rain-limited (Fig. 1g), or humidity-limited and winters generally cold-limited (Fig. 1b, g). In these locations, the shoulder seasons are mild with daily probabilities of mild weather as high as 0.75.
3.2 Changes in response to radiative forcing
The general pattern of changes from HiFLOR is similar to that found in an ensemble of CMIP5 models (Suppl. Fig. 6, pattern correlation with Fig. 2a of 0.86) and in a linearized climate change experiment using MERRA-2 reanalysis data (Suppl. Fig. 7a, pattern correlation with Fig. 2a of 0.93). However, regional differences exist, most significantly over regions of complex orography that are represented at higher resolution only (see Section 2). An increase in the number of days with mild weather is projected for mountainous areas globally, mostly caused by locally increasing temperatures (Suppl. Fig. 5a).
Seasonal shifts are also visible in the figures showing the annual distribution of mild weather for selected locations. Increasing humidity and maximum temperatures further limit the number of days with mild weather in tropical winters (Fig. 2d, e). The mid-latitudes are projected to have slightly more mild summer days because of increased temperatures, though precipitation limits the total increase (Fig. 2c). The locations with two mild shoulder seasons show relatively small changes in the total number of days with mild weather. However, summers are projected to become less mild because of humidity increases, whereas winters are projected to become more mild because of increasing temperatures (Fig. 2b, f, g).
The above-described changes are for the end of the 21st century based on RCP4.5, but HiFLOR projects that changes should be evident even in the near future (2016–2035) with a similar pattern of change (Suppl. Fig. 8, pattern correlation with Fig. 2a is 0.96). Though the projected changes are stronger at the end of the century, in the next two decades, a global mean decrease of 4 mild days per year is projected (−5%). Shifts in seasonality, similar but of smaller magnitude than to those described for the end of the century, are projected for individual locations in the near future.
3.3 Interannual variability
To understand how the magnitude of the projected changes in the occurrence of mild weather measures against year-to-year variability, we investigate the interannual variability of the global number of mild days using the ensemble of historical integrations. Atmosphere-ocean coupled variability forces a slight increase in interannual variability; the standard deviation increases from 0.96 mild days per year in the control experiment to 1.61 mild days per year in the ensemble mean of the historical experiment (Suppl. Fig. 9b). The variability in the historical experiment is closer to that found in the MERRA-2 reanalysis as may be expected (1.96 days per year).
There is a weak relation between mild weather occurrence and ENSO. Using linear regression, we find that moderate El Niño events lead to a global decrease of 1 mild day per year (Suppl. Fig. 9c). The strongest decreases are found in central Mexico, eastern Brazil, northern Australia, Namibia and Botswana; increases are found in eastern Australia and South Africa. These regions see a similar but stronger change in response to RCP4.5 (Fig. 2a). Locally, different criteria may be responsible for the observed relation and individual criteria may have opposing effects. For example, in Australia, temperature increases result in a decreasing number of mild days during El Niño events, but precipitation and humidity decreases result in an increasing numbers of mild days.
3.4 Changes in the recent past
We have presented the first analysis of mild weather occurrence and projected changes with respect to climate change. The analysis is based on simple criteria that may be refined or extended in future work. Such refinements could include additional variables, for example, cloudiness or wind speed, or by defining spatially or seasonally varying criteria. Furthermore, the uncertainty of the projections related to scenario choice could be part of a future assessment. The current analysis is based on RCP4.5, the projected changes are likely to be larger in response to RCP8.5 that prescribes stronger radiative forcing.
In conclusion, the global mean number of mild days in a year is projected to decrease in response to radiative forcing. The largest decreases are found in the tropics and subtropics whereas the mid-latitudes are projected to have a small increase. Furthermore, many locations are projected to experience shifts in the seasonal distribution of mild weather; summers lose mild weather days while winters gain mild weather days. Notably, the projected changes in response to radiative forcing are larger than the interannual variability associated with ENSO and the pattern of observed changes matches model projections. Though the changes are larger at the end of the century, shifts in the global distribution and seasonality of mild weather are already projected for the next decades. Similar to findings of changes in economic damages, countries in the tropics are projected to be negatively impacted by climate change (Benson and Clay 2000; Lemoine and Kapnick 2015).
This is the first global investigation of the annual variation of mild weather and its response to projected radiative forcing. Most studies that investigate climate change impacts investigate changes in the mean state, which the public has difficulty relating to (Weber 2010), or climate extremes, that occur rarely and have a strong negative connotation. In contrast, mild weather is a positive concept and occurs frequently. Given the significance of mild weather for human outdoor activity and the fact that mild weather is highly relatable to the public (mild weather may be considered to be “pleasant weather”), our results provide local, near future, and personally relevant climate change information. The presented information may therefore be used to communicate climate variability and climate change impacts to a broad audience. Given its association with various activities, it should also be explored for having an impact on various industries (e.g., tourism, outdoor events, construction, and transport). We hypothesize that regional changing characteristics of mild weather may be detectable and attributable to climate change at a relatively early stage because of the frequent occurrence of mild weather and the relative size of projected change vs. noted internal variability (Hegerl et al. 2006; Stott et al. 2010).
Given the substantial spatial and temporal redistributions of mild weather in response to radiative forcing and potential impacts on human activity, the authors suggest that the field of climate science extends its focus to include frequently occurring and relatable weather conditions. Furthermore, existing research on the physiological effects of meteorological conditions (including for example physical and mental health studies, leisure studies, and urban planning) may be extended to consider the occurrence of and changes in mild weather. For these reasons, improving our understanding of mild weather should be of high scientific interest.
Models included: ACCESS1-0, ACCESS1-3, CanESM2, CCSM4, CMCC-CM, CSIRO-Mk3-6-0, GFDL-CM3, GFDL-ESM2G, GFDL-ESM2M, GISS-E2-H, GISS-E2-R, HadGEM2-CC, HadGEM2-ES, MIROC-ESM, MIROC-ESM-CHEM, MPI-ESM-LR, NorESM1-M
Models included: BNU-ESM, CNRM-CM5, GFDL-ESM2M, HadGEM2-CC, HadGEM2-ES, IPSL-CM5A-LR, IPSL-CM5A-MR, IPSL-CM5B-LR, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3, NorESM1-M
http://www.esrl.noaa.gov/psd/enso/mei/, accessed 16 May 2016
The authors would like to thank Keith Dixon and Marjolein van Huijgevoort for comments on an early version of the manuscript. Three anonymous reviewers are thanked for their comments that helped to improve the manuscript. Funding for this work was supplied by the National Oceanic and Atmospheric Administration, U.S. Department of Commerce to the Geophysical Fluid Dynamics Laboratory, to the Cooperative Institute for Climate Science (award NA14OAR4320106), and to the University Corporation for Atmospheric Research. The statements, findings, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the views of the National Oceanic and Atmospheric Administration, or the U.S. Department of Commerce. We acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output.
Compliance with ethical standards
All authors contributed to the design of the study, discussion of the results, and writing of the manuscript. G.A.V. set up the model experiments. K.v.d.W. performed the analyses.
Conflict of interests
The authors declare that they have no conflict of interests.
- Benson C, Clay EJ (2000) Developing countries and the economic impacts of natural disasters. Managing disaster risk in emerging economies, 11-21Google Scholar
- CIESIN (2015) Gridded Population of the World, Version 4 (GPWv4): population density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/ 10.7927/H46T0JKB
- Hegerl GC, Karl TR, Allen M, Bindoff NL, Gillett N, Karoly D, Zhang X, Zwiers, F (2006) Climate change detection and attribution: beyond mean temperature signals. J Clim 19:5058–5077Google Scholar
- IPCC (2012) Managing the risks of extreme events and disasters to advance climate change adaptation. A special report of working groups I and II of the intergovernmental panel on climate change [Field CB, Barros V, Stocker TF, Qin D, Dokken DJ, Ebi KL, Mastrandrea MD, Mach KJ, Plattner G-K, Allen SK, Tignor M, Midgley PM (eds)]. Cambridge University Press, Cambridge, UK, and New York, NY, USA, 582 ppGoogle Scholar
- IPCC (2013) Climate change 2013: the physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change [Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, 1535 ppGoogle Scholar
- Knez I, Thorsson S, Eliasson I, Lindberg F (2009) Psychological mechanisms in outdoor place and weather assessment: towards a conceptual model. Int J Biometeorol 53:101–111Google Scholar
- Koster RD, Bosilovich MG, Akella S, Lawrence C, Cullather R, Draper C, Gelaro R, Kovach R, Liu Q, Molod A, Norris P, Wargan K, Chao W, Reichle R, Takacs L, Todling R, Vikhliaev Y, Bloom S, Collow A, Partyka G, Labow G, Pawson S, Reale O, Schubert S, Suarez M (2015) MERRA-2: Initial evaluation of the climate. NASA Tech. Rep. Series on Global Modeling and Data Assimilation 43, 153 pp. Available online at https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20160005045.pdf
- Lemoine D, Kapnick SB (2015) A top-down approach to projecting market impacts of climate change. Nature Climate ChangeGoogle Scholar
- Murakami H, Vecchi GA, Underwood S, Delworth TL, Wittenberg AT, Anderson WG, Chen J-H, Gudgel, RG, Harris LM, Lin S-J, Zeng F (2015) Simulation and prediction of category 4 and 5 hurricanes in the high-resolution GFDL HiFLOR coupled climate model. J Clim 28:9058–9079Google Scholar
- Stott PA, Gillett NP, Hegerl GC, Karoly DJ, Stone DA, Zhang X, Zwiers F (2010) Detection and attribution of climate change: a regional perspective. Wiley Interdiscip Rev Clim Chang 1:192–211Google Scholar
- Van der Wiel K, Kapnick SB, Vecchi GA, Cooke WF, Delworth TL, Jia L, Murakami H, Underwood S, Zeng F (2016) The resolution dependence of U.S. precipitation extremes in response to CO2 forcing. J Clim 29:7991–8012Google Scholar
- Van Vuuren DP, Edmonds J, Kainuma M, Riahi K, Thomson A, Hibbard K, Hurtt GC, Kram T, Krey V, Lamarque J-F, Masui T, Meinshausen M, Nakicenovic N, Smith SJ, Rose SK (2011) The representative concentration pathways: an overview. Clim Chang 109:5–31Google Scholar
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