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Seasonality of biological and physical systems as indicators of climatic variation and change

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Abstract

Evidence-based responses to climate change by society require operational and sustained information including biophysical indicator systems that provide up-to-date measures of trends and patterns against historical baselines. Two key components linking anthropogenic climate change to impacts on socio-ecological systems are the periodic inter- and intra-annual variations in physical climate systems (seasonality) and in plant and animal life cycles (phenology). We describe a set of national indicators that reflect sub-seasonal to seasonal drivers and responses of terrestrial physical and biological systems to climate change and variability at the national scale. Proposed indicators and metrics include seasonality of surface climate conditions (e.g., frost and freeze dates and durations), seasonality of freeze/thaw in freshwater systems (e.g., timing of stream runoff and durations of lake/river ice), seasonality in ecosystem disturbances (e.g., wildfire season timing and duration), seasonality in vegetated land surfaces (e.g., green-up and brown-down of landscapes), and seasonality of organismal life-history stages (e.g., timings of bird migration). Recommended indicators have strong linkages to variable and changing climates, include abiotic and biotic responses and feedback mechanisms, and are sufficiently simple to facilitate communication to broad audiences and stakeholders interested in understanding and adapting to climate change.

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References

  • Abatzoglou JT, Williams AP (2016) Impact of anthropogenic climate change on wildfire across western US forests. Proc Natl Acad Sci U S A 113:11770–11775

    Google Scholar 

  • Ault TR, Henebry GM, de Beurs KM et al (2013) The false spring of 2012, earliest in North America record. Eos 94:181–183

    Google Scholar 

  • Ault TR, Schwartz MD, Zurita-Milla R et al (2015) Trends and natural variability of spring onset in the coterminous United States as evaluated by a new gridded dataset of spring indices. J Clim 28:8363–8378

    Google Scholar 

  • Bieniek PA, Bhatt US, Rundquist LA et al (2011) Large-scale climate controls of interior Alaska river ice breakup. J Clim 24:286–297

    Google Scholar 

  • Bojinski S, Verstraete M, Peterson TC et al (2014) The concept of essential climate variables in support of climate research, applications, and policy. Bull Am Meteorol Soc 95:1431–1443

    Google Scholar 

  • Bradford JB, Weltzin JF, McCormick M et al. (2020) Ecological forecasting: 21st century science for 21st century management. U.S. Geological Survey Open-File Report 2020–1073 /https://doi.org/10.3133/ofr20201073

  • Brand SPC, Keeling MJ (2017) The impact of temperature changes on vector-borne disease transmission: Culicoides midges and bluetongue virus. J R Soc Interface 14:20160481

    Google Scholar 

  • Buckley LB, Foushee MS (2012) Footprints of climate change in US national park visitation. Int J Biometeorol 56:1173–1177

    Google Scholar 

  • Buizer JL, Fleming P, Hays SL et al. (2013) Report on preparing the nation for change: building a sustained national climate assessment process. National Climate Assessment and Development Advisory Committee

  • Cleland EE, Allen JM, Crimmins TM et al (2012) Phenological tracking enables positive species responses to climate change. Ecology 93:1765–1771

    Google Scholar 

  • Cohen JM, Lajeunesse MJ, Rohr JR (2018) A global synthesis of animal phenological responses to climate change. Nat Clim Chang 8:224–228

    Google Scholar 

  • Cook BI, Cook ER, Huth PC et al (2008) A cross-taxa phenological dataset from Mohonk Lake, NY and its relationship to climate. Int J Climatol 28:1369–1383

    Google Scholar 

  • Crimmins TM, Crimmins MA, Bertelsen CD (2010) Complex responses to climate drivers in onset of spring flowering across a semi-arid elevation gradient. J Ecol 98:1042–1051

    Google Scholar 

  • Crimmins TM, Gerst KL, Huerta DG et al (2020) Short-term forecasts of insect phenology inform pest management. Ann Entomol Soc Am 113:139–148

    Google Scholar 

  • Crimmins TM, Marsh RL, Switzer J et al. (2017) USA National Phenology Network gridded products documentation. U.S. Geological Survey Open-File Report 2017–1003. https://doi.org/10.3133/ofr20171003

  • Dietze MC et al (2018) Iterative near-term ecological forecasting: needs, opportunities, and challenges. Proc Natl Acad Sci U S A 115:1424–1432

    Google Scholar 

  • Dudley RW, Hodgkins GA, McHale et al (2017) Trends in snowmelt-related streamflow timing in the conterminous United States. J Hydrol 547:208–221

    Google Scholar 

  • Duffy JE, Amaral-Zettler LA, Fautin DG et al (2013) Envisioning a marine biodiversity observation network. BioScience 63:350–361

    Google Scholar 

  • Eidenshink J, Schwind B, Brewer K et al (2007) A project for monitoring trends in burn severity. Fire Ecol 3:3–21

    Google Scholar 

  • Enquist CAF, Kellermann JL, Gerst KL et al (2014) Phenology research for natural resource management in the United States. Int J Biometeorol 58:579–589

    Google Scholar 

  • EPA (2016) Climate change indicators in the United States. Fourth edition. EPA 430-R-16-004. https://doi.org/10.13140/RG.2.2.30480.20487. Accessed 21 August 2018

  • Fisichelli NA, Schuurman GW, Monahan WB et al (2015) Protected area tourism in a changing climate: will visitation at US National Parks warm up or overheat? PLoS One 10:e0128226

    Google Scholar 

  • Hampton SE, Strasser CA, Tewksbury JJ et al (2013) Big data and the future of ecology. Front Ecol Environ 11:156–162

    Google Scholar 

  • Hobbins M, Wood A, McEvoy D et al (2016) The evaporative demand drought index: part I – linking drought evolution to variations in evaporative demand. J Hydrometeorol 17:1745–1761

    Google Scholar 

  • IPCC (2014) Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Field CB, VR Barros, DJ Dokken, KJ Mach, MD Mastrandrea, TE Bilir, M Chatterjee, KL Ebi, YO Estrada, RC Genova, B Girma, ES Kissel, AN Levy, S MacCracken, PR Mastrandrea, and LLWhite (eds) Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1132 pp

  • Jackson ST, Duke CS, Hampton SE et al (2016) Toward a national, sustained U.S. ecosystem assessment. Science 354:6314

    Google Scholar 

  • Jochner S, Caffarra A, Menzel A (2013) Can spatial data substitute temporal data in phenological modelling? A survey using birch flowering. Tree Physiol 33:1256–1268

    Google Scholar 

  • Jones KB, Bogena H, Vereecken H et al (2010) Design and importance of multi-tiered ecological monitoring networks. In: Müller F et al (eds) Long-term ecological research. Springer Science+Business Media B.V., pp 355–374

  • Jolly W, Cochrane M, Freeborn P et al (2015) Climate-induced variations in global wildfire danger from 1979 to 2013. Nat Commun 6:7537

    Google Scholar 

  • Keatinge WR (2003) Death in heat waves. BMJ 327:512

    Google Scholar 

  • Kenney MA, Janetos AC et al. (2014) National climate indicators system report. National Climate Assessment Development and Advisory Committee

    Google Scholar 

  • Kenney MA, Janetos AC, Lough GC (2016) Building an integrated U.S. national climate indicators system. Clim Chang 135:85

    Google Scholar 

  • Kenney MA, Janetos AC, Gerst MD (2018) A framework for national climate indicators. Clim Chang. https://doi.org/10.1007/s10584-018-2307-y

  • Kim Y, Kimball JS, Glassy J, Du J (2017) An extended global earth system data record on daily landscape freeze-thaw status determined from satellite passive microwave remote sensing. Earth Syst Sci Data 9:133–147

    Google Scholar 

  • Kim Y, Kimball JS, Zhang K et al (2012) Satellite detection of increasing northern hemisphere non-frozen seasons from 1979 to 2008: implications for regional vegetation growth. Remote Sens Environ 121:472–487

    Google Scholar 

  • Kukal MS, Irmak S (2018) US agro-climate in 20th century: growing degree days, first and last frost, growing season length, and impacts on crop yields. Nat Sci Rep 8:6977

    Google Scholar 

  • Lafferty KD (2009) The ecology of climate change and infectious diseases. Ecology 90:888–900

    Google Scholar 

  • Lawler JJ (2009) Climate change adaptation strategies for resource management and conservation planning. Ann N Y Acad Sci 1162:79–98

    Google Scholar 

  • Liebhold AM (2012) Forest pest management in a changing world. Int J Pest Manag 58:289–295

    Google Scholar 

  • Lipton D, Rubenstein MA, Weiskopf SR, Carter S, Peterson J, Crozier L, Fogarty M, Gaichas S, Hyde KJW, Morelli TL, Morisette J, Moustahfid H, Muñoz R, Poudel R, Staudinger MD, Stock C, Thompson L, Waples R, Weltzin JF (2018) Ecosystems, ecosystem services, and biodiversity. In: Reidmiller DR, Avery CW, Easterling DR, Kunkel KE, Lewis KLM, Maycock TK, Stewart BC (eds) Impacts, risks, and adaptation in the United States: fourth National Climate Assessment, volume II. U.S. Global Change Research Program, Washington, DC, pp 268–321

    Google Scholar 

  • Magnuson JJ et al (2000) Historical trends in lake and river ice cover in the northern hemisphere. Science 289:1743–1746

    Google Scholar 

  • Martinuzzi S, Allstadt AJ, Pidgeon AM, Flather CH, Jolly WM, Radeloff VC (2019) Future changes in fire weather, spring droughts, and false springs across U.S. National Forests and Grasslands. Ecol Appl 29:e01904

    Google Scholar 

  • Mason LA, Riseng CM, Gronewold AD et al (2016) Fine-scale spatial variation in ice cover and surface temperature trends across the surface of the Laurentian Great Lakes. Clim Chang 138:71–83

    Google Scholar 

  • McCabe GJ, Clark MM (2005) Trends and variability in snowmelt runoff in the western United States. J Hydrometeorol 6:476–482

    Google Scholar 

  • McDonald KW, McClure CJW, Rolek BW et al (2012) Diversity of birds in eastern North America shifts north with global warming. Ecol Evol 2:3052–3060

    Google Scholar 

  • Mehdipoor H, Zurita-Milla R, Augustijn EW, Izquierdo-Verdiguier E (2019) Exploring differences in spatial patterns and temporal trends of phenological models at continental scale using gridded temperature time-series. Int J Biometeorol 64:409–421

    Google Scholar 

  • Melillo JM, Richmond TC, Yohe GW eds. (2014) Climate change impacts in the United States: the third National Climate Assessment. 841 pp. U.S. Global Change Research Program, Washington, DC

  • Menne MJ, Durre I, Vose RS et al (2012) An overview of the global historical climatology network-daily database. J Atmos Ocean Technol 29:897–910

    Google Scholar 

  • Miloslavich P, Bax NJ, Simmons SE et al (2018) Essential ocean variables for global sustained observations of biodiversity and ecosystem changes. Glob Chang Biol 24:2416–2433

    Google Scholar 

  • Mononen LA, Auvinen P, Ahokumpu AL et al (2016) National ecosystem service indicators: measures of social–ecological sustainability. Ecol Indic 61:27–37

    Google Scholar 

  • Moon M, Zhang X, Henebry GM et al (2019) Long-term continuity in land surface phenology measurements: a comparative assessment of the MODIS land cover dynamics and VIIRS land surface phenology products. Remote Sens Environ 226:74–92

    Google Scholar 

  • Muller-Karger FE, Miloslavich P, Bax NJ et al (2018) Advancing marine biological observations and data requirements of the complementary Essential Ocean Variables (EOVs) and Essential Biodiversity Variables (EBVs) frameworks. Front Mar Sci 5:211

    Google Scholar 

  • National Academies of Science (2016) Next generation earth system prediction: strategies for subseasonal to seasonal forecasts. 350 pp. National Academies Press, Washington, DC

  • Paquin D, de Elía R, Bleau S et al (2016) A multiple timescales approach to assess urgency in adaptation to climate change with an application to the tourism industry. Environ Sci Pol 63:143–150

    Google Scholar 

  • Parmesan C (2007) Influences of species, latitudes and methodologies on estimates of phenological response to global warming. Glob Chang Biol 13:1860–1872

    Google Scholar 

  • Pau S, Wolkovich EM, Cook BI et al (2011) Predicting phenology by integrating ecology, evolution and climate science. Glob Chang Biol 17:3633–3643

    Google Scholar 

  • Pereira HM, Ferrier S, Walters M et al (2013) Essential biodiversity variables. Science 339:277–278

    Google Scholar 

  • Pinsky ML, Worm B, Fogarty MJ et al (2013) Marine taxa track local climate velocities. Science 341:1239–1242

    Google Scholar 

  • Poloczanska ES, Brown CJ, Sydeman WJ et al (2013) Global imprint of climate change on marine life. Nat Clim Chang 3:919–925

    Google Scholar 

  • Reidmiller DR, Avery CW, Easterling DR et al. (2018) Impacts, risk, and adaptation in the United States: fourth National Climate Assessment, volume II. 1515 pp. U.S., global change research program, Washington, DC

  • Reges HW, Doesken N, Turner J, Newman N (2016) CoCoRaHS: the evolution and accomplishments of a volunteer rain gauge network. Bull Am Meteorol Soc 97:1831–1846 https://journals.ametsoc.org/bams/article/97/10/1831/69656

  • Richardson AD, Keenan TF, Migliavacca M et al (2013) Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agric For Meteorol 169:156–173

    Google Scholar 

  • Rosemartin AH, Crimmins TM, Enquist CAF et al (2014) Organizing phenological data resources to inform natural resource conservation. Biol Conserv 173:90–97. https://doi.org/10.1016/j.biocon.2013.07.003

    Article  Google Scholar 

  • Sagarin R, Micheli F (2001) Climate change in nontraditional data sets. Science 294:811

    Google Scholar 

  • Sapkota A, Murtugudde R, Curriero FC et al (2019) Associations between alteration in plant phenology and hay fever prevalence among US adults: implication for changing climate. PLoS One 14(3)

  • Sapkota A, Dong Y, Li L et al (2020) Association between changes in timing of spring onset and asthma hospitalization in Maryland. JAMA Netw Open 3:e207551

    Google Scholar 

  • Schwartz MD, Ahas R, Aasa A (2006) Onset of spring starting earlier across the northern hemisphere. Glob Chang Biol 12:343–351

    Google Scholar 

  • Schwartz MD, Betancourt JL, Weltzin JF (2012) From Caprio’s lilacs to the USA National Phenology Network. Front Ecol Environ 10:324–327

    Google Scholar 

  • Seifert CA, Lobell DB (2015) Response of double cropping suitability to climate change in the United States. Environ Res Lett 10:024002

    Google Scholar 

  • Staudinger M, Mills KE, Stamieszkin K et al (2019) It’s about time: a synthesis of changing phenology in the Gulf of Maine ecosystem. Fish Oceanogr 28:532–566

    Google Scholar 

  • USGCRP (2015) National Climate Assessment & Development Advisory Committee (2011–2014) Meetings, decisions, and adopted documents

  • Weber RW (2012) Impact of climate change on aeroallergens. Ann Allergy Asthma Immunol 108:294–299

    Google Scholar 

  • Westerling ALR (2016) Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring. Philos Trans R Soc B 371:20150178

    Google Scholar 

  • White CJ, Carlsen H, Robertson AW et al (2017) Potential applications of subseasonal-to-seasonal (S2S) predictions. Meteorol Appl 24:315–325

    Google Scholar 

  • Wolkovich EM, Cleland EE (2010) The phenology of plant invasions: a community ecology perspective. Front Ecol Environ 9:287–294

    Google Scholar 

  • Wolkovich EM, Cook BL, Allen JM et al (2012) Warming experiments underpredict plant phenological responses to climate change. Nature 485:494–497

    Google Scholar 

  • Zhang Y, Bielory L, Cai T et al (2015) Predicting onset and duration of airborne allergenic pollen season in the United States. Atmos Environ 103:297–306

    Google Scholar 

  • Zhang X, Jayavelu S, Liu L et al (2018) Evaluation of land surface phenology from VIIRS data using time series of PhenoCam imagery. Agric For Meteorol 256-257:137–149

    Google Scholar 

  • Ziska L, Knowlton K, Rogers C et al (2011) Recent warming by latitude associated with increased length of ragweed pollen season in Central North America. Proc Natl Acad Sci U S A 108:4248–4251

    Google Scholar 

  • Zuckerberg B, Strong CM, LaMontagne JM et al (2020) Climate dipoles as continental drivers of plant and animal populations. Trends Ecol Evol. https://doi.org/10.1016/j.tree.2020.01.010

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Acknowledgments

The authors acknowledge the support provided by A.C. Janetos, chair of the Indicator Work Group (IWG) under the National Climate Assessment and Development Advisory Committee (NCADAC). Members of the Indicators Technical Teams, NCADAC IWG, and Kenney’s NCIS research team are included in Kenney et al. (2014). Earlier versions of this report were reviewed by K. Bruce Jones. Amanda Staudt, the IWG Forest Indicators Technical Team led by Linda Heath, and members of the IWG. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. government. This publication honors of Tony Janetos, who constantly challenged our team to think bigger and to assure we were rigorous in our vision.

Funding

Kenney’s research team provided research and coordination support to the technical team, which was supported by National Oceanic and Atmospheric Administration grant NA09NES4400006 and NA14NES4320003 (Cooperative Climate and Satellites-CICS) at the University of Maryland/ESSIC.

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Authors and Affiliations

  1. U.S. Geological Survey, 2150 Centre Ave, Bldg C, Fort Collins, CO, USA

    Jake F. Weltzin

  2. U.S. Geological Survey, 12201 Sunrise Valley Dr., MS-913, Reston, VA, 20192, USA

    Julio L. Betancourt

  3. Goddard Institute for Space Studies, National Aeronautics and Space Administration, 2880 Broadway, New York, NY, 10025, USA

    Benjamin I. Cook

  4. USA National Phenology Network/School of Natural Resources and the Environment, University of Arizona, 1311 E 4th St, Tucson, AZ, USA

    Theresa M. Crimmins

  5. U.S. Geological Survey, University of Arizona, 1064 Lowell Street, Tucson, AZ, 85721-0137, USA

    Carolyn A. F. Enquist

  6. Earth System Science Interdisciplinary Center/Cooperative Institute for Climate and Satellites-Maryland, University of Maryland, 5825 University Research Court, Suite 4001, College Park, MD, 20740-3823, USA

    Michael D. Gerst

  7. U.S. National Park Service, 1201 Oak Ridge Drive, Suite 200, Fort Collins, CO, 80525, USA

    John E. Gross

  8. Department of Geography, Environment, and Spatial Sciences, Michigan State University, East Lansing, MI, 48824, USA

    Geoffrey M. Henebry

  9. Denver Botanic Gardens, 909 York Street, Denver, CO, 80206, USA

    Rebecca A. Hufft

  10. Institute on the Environment, University of Minnesota, 1954 Buford Ave., Suite 325, St. Paul, MN, 55108, USA

    Melissa A. Kenney

  11. Numerical Terradynamic Simulation Group, W.A. Franke College of Forestry & Conservation, The University of Montana, Missoula, MT, 59812, USA

    John S. Kimball

  12. US Geological Survey, Reston, VA, 20192, USA

    Bradley C. Reed

  13. Ecosystem and Conservation Sciences, W.A. Franke College of Forestry & Conservation, The University of Montana, Missoula, MT, 59812, USA

    Steven W. Running

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  1. Jake F. Weltzin
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  2. Julio L. Betancourt
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  3. Benjamin I. Cook
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  8. Geoffrey M. Henebry
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  9. Rebecca A. Hufft
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  10. Melissa A. Kenney
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  11. John S. Kimball
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  13. Steven W. Running
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Correspondence to Theresa M. Crimmins.

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This article is part of a Special Issue on “National Indicators of Climate Changes, Impacts, and Vulnerability” edited by Anthony C. Janetos and Melissa A. Kenney

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Weltzin, J.F., Betancourt, J.L., Cook, B.I. et al. Seasonality of biological and physical systems as indicators of climatic variation and change. Climatic Change 163, 1755–1771 (2020). https://doi.org/10.1007/s10584-020-02894-0

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