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Non-analog increases to air, surface, and belowground temperature extreme events due to climate change

Abstract

Air temperatures (Ta) are rising in a changing climate, increasing extreme temperature events. Examining how Ta increases are influencing extreme temperatures at the soil surface and belowground in the soil profile can refine our understanding of the ecological consequences of rising temperatures. In this paper, we validate surface and soil temperature (Ts: 0–100-cm depth) simulations in the SOILWAT2 model for 29 locations comprising 5 ecosystem types in the central and western USA. We determine the temperature characteristics of these locations from 1980 to 2015, and explore simulations of Ta and Ts change over 2030–2065 and 2065–2100 time periods using General Circulation Model (GCM) projections and the RCP 8.5 emissions scenario. We define temperature extremes using a nonstationary peak over threshold method, quantified from standard deviations above the mean (0-σ: an event \(>\sim \) 51% of extreme events; 2-\(\sigma :>\sim 98\%\)). Our primary objective is to contrast the magnitude (C) and frequency of occurrence of extreme temperature events between the twentieth and twenty-first century. We project that temperatures will increase substantially in the twenty-first century. Extreme Ta events will experience the largest increases by magnitude, and extreme Ts events will experience the largest increases by proportion. On average, 2-σ extreme Ts events will increase by 3.4 C in 2030–2065 and by 5.3 C in 2065–2100. Increases in extreme Ts events will often exceed + 10 C at 0–20 cm by 2065–2100, and at 0–100 cm will often exceed 5.0 standard deviations above 1980–2015 values. 2-σ extreme Ts events will increase from 0.9 events per decade in 1980–2015 to 23 events in 2030–2065 and 38 events in 2065–2100. By 2065–2100, the majority of months will experience extreme events that co-occur at 0–100 cm, which did not occur in 1980–2015. These projections illustrate the non-analog temperature increases that ecosystems will experience in the twenty-first century as a result of climate change.

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References

  • Alster C, von Fischer J, Allison S, Treseder K (2020) Embracing a new paradigm for temperature sensitivity of soil microbes. Glob Change Biol. https://doi.org/10.1111/gcb.15053

  • Anderegg W, Trugman A, Bowling D, Salvucci G, Tuttle S (2019) Plant functional traits and climate influence drought intensification and land–atmosphere feedbacks. Proc Natl Acad Sci USA 116:14071–14076. https://doi.org/10.1073/pnas.1904747116

    Google Scholar 

  • Barron-Gafford GA, Scott RL, Jenerette GD, Hamerlynck EP, Huxman TE (2012) Temperature and precipitation controls over leaf- and ecosystem-level CO2 flux along a woody plant encroachment gradient. Glob Change Biol 18:1389–1400. https://doi.org/10.1111/j.1365-2486.2011.02599.x

    Google Scholar 

  • Bell D, Bradford J, Lauenroth W (2014) Early indicators of change: divergent climate envelopes between tree life stages imply range shifts in the western United States. Glob Ecol Biogeogr 23:168–180. https://doi.org/10.1111/geb.12109

    Google Scholar 

  • Belnap J (2002) Nitrogen fixation in biological soil crusts from southeast Utah, USA. Biol Fertil Soils 35:128–135. https://doi.org/10.1007/s00374-002-0452-x

    Google Scholar 

  • Berard A, Bouchet T, Sevenier G, Pablo A, Gros R (2011) Resilience of soil microbial communities impacted by severe drought and high temperature in the context of Mediterranean heat waves. Eur J Soil Biol 47:333–342. https://doi.org/10.1016/j.ejsobi.2011.08.004

    Google Scholar 

  • Betts R, Cox P, Woodward F (2000) Simulated responses of potential vegetation to doubled-CO2 climate change and feedbacks on near-surface temperature. Glob Ecol Biogeogr 9:171–180. https://doi.org/10.1046/j.1365-2699.2000.00160.x

    Google Scholar 

  • Bradford J, Bell D (2017) A window of opportunity for climate-change adaptation: easing tree mortality by reducing forest basal area. Front Ecol Environ 15:11–17. https://doi.org/10.1002/fee.1445

    Google Scholar 

  • Bradford J, Schlaepfer D, Lauenroth W (2014a) Ecohydrology of adjacent sagebrush and Lodgepole pine ecosystems: the consequences of climate change and disturbance. Ecosystems 17:590–605. https://doi.org/10.1007/s10021-013-9745-1

    Google Scholar 

  • Bradford J, Schlaepfer D, Lauenroth W, Burke I (2014b) Shifts in plant functional types have time-dependent and regionally variable impacts on dryland ecosystem water balance. J Ecol 102:1408–1418. https://doi.org/10.1111/1365-2745.12289

    Google Scholar 

  • Bradford J, Schlaepfer D, Lauenroth W, Yackulic C, Duniway M, Hall S, Jia G, Jamiyansharav K, Munson S, Wilson S, Tietjen B (2017) Future soil moisture and temperature extremes imply expanding suitability for rainfed agriculture in temperate drylands. Sci Rep 7:12923. https://doi.org/10.1038/s41598-017-13165-x

    Google Scholar 

  • Bradford M, Wieder W, Bonan G, Fierer N, Raymond P, Crowther T (2016) Managing uncertainty in soil carbon feedbacks to climate change. Nat Clim Change 6:751–758. https://doi.org/10.1038/NCLIMATE3071

    Google Scholar 

  • Brunsell N A, Gillies R R (2003) Scale issues in land-atmosphere interactions: implications for remote sensing of the surface energy balance. Agric For Meterol 117:203–221. https://doi.org/10.1016/S0168-1923(03)00064-9

    Google Scholar 

  • Buckley L, Huey R (2016) Temperature extremes: geographic patterns, recent changes, and implications for organismal vulnerabilities. Glob Change Biol 22:3829–3842. https://doi.org/10.1111/gcb.13313

    Google Scholar 

  • Burnham K, Anderson D (2002) Model selection and multimodel inference, vol 1. Springer, New York

    Google Scholar 

  • Burnham K, Anderson D (2004) Multimodel inference—understanding AIC and BIC in model selection. Sociol Methods Res 33:261–304. https://doi.org/10.1177/0049124104268644

    Google Scholar 

  • Chatfield C (1995) Model uncertainty, data mining and statistical-inference. J R Stat Soc Ser A - Stat Soc 158:419–466. https://doi.org/10.2307/2983440

    Google Scholar 

  • Choler P (2018) Winter soil temperature dependence of alpine plant distribution: implications for anticipating vegetation changes under a warming climate. Perspect Plant Ecol Evol System 30:6–15. https://doi.org/10.1016/j.ppees.2017.11.002

    Google Scholar 

  • Chung Y, Thornton B, Dettweiler-Robinson E, Rudgers J (2019) Soil surface disturbance alters cyanobacterial biocrusts and soil properties in dry grassland and shrubland ecosystems. Plant Soil 441:147–159. https://doi.org/10.1007/s11104-019-04102-0

    Google Scholar 

  • Coelho C A S, Ferro C A T, Stephenson D B (2008) Methods for exploring spatial and temporal variability of extreme events in climate data. J Clim 21:2072–2092. https://doi.org/10.1175/2007JCLI1781.1

    Google Scholar 

  • Collins S, Ladwig L, Petrie M, Jones S, Mulhouse J, Thibault J, Pockman W (2017) Press–pulse interactions: effects of warming, N deposition, altered winter precipitation, and fire on desert grassland community structure and dynamics. Glob Change Biol 45, in press. https://doi.org/10.1111/gcb.13493

  • Darrouzet-Nardi A, Reed S, Grote E, Belnap J (2015) Observations of net soil exchange of CO2 in a dryland show experimental warming increases carbon losses in biocrust soils. Biogeochemistry 126:363–378. https://doi.org/10.1007/s10533-015-0163-7

    Google Scholar 

  • De Frenne P, Rodriguez-Sanchez F, Coomes D, Baeten L, Verstraeten G, Vellend M, Bernhardt-Roemermann M, Brown C, Brunet J, Cornelis J, Decocq G, Dierschke H, Eriksson O, Gilliam F, Hedl R, Heinken T, Hermy M, Hommel P, Jenkins M, Kelly D, Kirby K, Mitchell F, Naaf T, Newman M, Peterken G, Petrik P, Schultz J, Sonnier G, Van Calster H, Waller D, Walther G, White P, Woods K, Wulf M, Graae B, Verheyen K (2013) Microclimate moderates plant responses to macroclimate warming. Proc Natl Acad Sci USA 110:18561–18565. https://doi.org/10.1073/pnas.1311190110

    Google Scholar 

  • Dell A, Pawar S, Savage V (2011) Systematic variation in the temperature dependence of physiological and ecological traits. Proc Natl Acad Sci USA 108:10591–10596. https://doi.org/10.1073/pnas.1015178108

    Google Scholar 

  • Diffenbaugh N, Pal J, Trapp R, Giorgi F (2005) Fine-scale processes regulate the response of extreme events to global climate change. Proc Natl Acad Sci USA 102:15774–15778. https://doi.org/10.1073/pnas.0506042102

    Google Scholar 

  • Dobrowski S, Swanson A, Abatzoglou J, Holden Z, Safford H, Schwartz M, Gavin D (2015) Forest structure and species traits mediate projected recruitment declines in western US tree species. Glob Ecol Biogeogr 24:917–927. https://doi.org/10.1111/geb.12302

    Google Scholar 

  • D’Odorico P, Bhattachan A, Davis KF, Ravi S, Runyan CW (2013) Global desertification: drivers and feedbacks. Adv Water Resour 51:326–344. https://doi.org/10.1016/j.advwatres.2012.01.013

    Google Scholar 

  • D’Odorico P, Fuentes J, Pockman W, Collins S, He Y, Medeiros J, DeWekker S, Litvak M (2010) Positive feedback between microclimate and shrub encroachment in the northern Chihuahuan Desert. Ecosphere 1:1–11

    Google Scholar 

  • Duveiller G, Fasbender D, Meroni M (2015) Revisiting the concept of a symmetric index of agreement for continuous datasets. Sci Rep 6:1–14. https://doi.org/10.1038/srep19401

    Google Scholar 

  • Eitzinger J, Parton W, Hartman M (2000) Improvement and validation of a daily soil temperature submodel for freezing/thawing periods. Soil Sci 165:525–534

    Google Scholar 

  • Franklin J, Davis FW, Ikegami M, Syphard AD, Flint LE, Flint AL, Hannah L (2013) Modeling plant species distributions under future climates: how fine scale do climate projections need to be? Glob Change Biol 19:473–483. https://doi.org/10.1111/gcb.12051

    Google Scholar 

  • Frey S, Hadley A, Johnson S, Schulze M, Jones J, Betts M (2016) Spatial models reveal the microclimatic buffering capacity of old-growth forests. Sci Adv 2: UNSP e1501392. https://doi.org/10.1126/sciadv.1501392

  • Garcia-Suarez A, Butler C (2006) Soil temperatures at Armagh Observatory, Northern Ireland, from 1904 to 2002. Int J Climatol 26:1075–1089. https://doi.org/10.1002/joc.1294

    Google Scholar 

  • Gutzler D, Robbins T (2011) Climate variability and projected change in the western United States: regional downscaling and drought statistics. Clim Dyn 37:835–849. https://doi.org/10.1007/s00,382-010-0838-7

    Google Scholar 

  • Hamdi S, Moyano F, Sall S, Bernoux M, Chevallier T (2013) Synthesis analysis of the temperature sensitivity of soil respiration from laboratory studies in relation to incubation methods and soil conditions. Soil Biol Biochem 115–126. https://doi.org/10.1016/j.soilbio.2012.11.012

  • Hamerlynck E, Huxman T, Loik M, Smith S (2000) Effects of extreme high temperature, drought and elevated CO2 on photosynthesis of the Mojave Desert evergreen shrub, Larrea tridentata. Plant Ecol 148:183–193. https://doi.org/10.1023/A:1009896111405

    Google Scholar 

  • He Y, D’Odorico P, De Wekker S (2015) The role of vegetation-microclimate feedback in promoting shrub encroachment in the northern Chihuahuan desert. Glob Change Biol 21:2141–2154. https://doi.org/10.1111/gcb.12856

    Google Scholar 

  • Henn B, Raleigh M, Fisher A, Lundquist J (2013) A comparison of methods for filling gaps in hourly near-surface air temperature data. J Hydrometeorol 14:929–945. https://doi.org/10.1175/JHM-D-12-027.1

    Google Scholar 

  • Henry H, Abedi M, Alados C, Beard K, Fraser L, Jentsch A, Kreyling J, Kulmatiski A, Lamb E, Sun W, Vankoughnett M, Venn S, Werner C, Beil I, Blindow I, Dahlke S, Dubbert M, Effinger A, Garris H, Gartzia M, Gebauer T, Arfin Khan M, Malyshev A, Morgan J, Nock C, Paulson J, Pueyo Y, Stover H, Yang X (2018) Increased soil frost versus summer drought as drivers of plant biomass responses to reduced precipitation: results from a globally coordinated field experiment. Ecosystems 21:1432–1444. https://doi.org/10.1007/s10021-018-0231-7

    Google Scholar 

  • Hufkens K, Keenan T, Flanagan L, Scott R, Bernacchi C, Joo E, Brunsell N, Verfaillie J, Richardson A (2016) Productivity of North American grasslands is increased under future climate scenarios despite rising aridity. Nat Clim Change 6:710–714. https://doi.org/10.1038/NCLIMATE2942

    Google Scholar 

  • IPCC (2013) Climate change 2013: I the physical science basis. Cambridge University Press, London

    Google Scholar 

  • Irina K, Ekaterina T, Ruzalia V, Ekaterina V, Marina V (2019) Effect of temperature on litter decomposition, soil microbial community structure and biomass in a mixed-wood forest in European Russia. Curr Sci 116:765–772. https://doi.org/10.18520/cs/v116/i5/765-772

    Google Scholar 

  • James J, Sheley R, Leger E, Adler P, Hardegree S, Gornish E, Rinella M (2019) Increased soil temperature and decreased precipitation during early life stages constrain grass seedling recruitment in cold desert restoration. J Appl Ecol, press. https://doi.org/10.1111/1365-2664.13508

  • Jentsch A, Beierkuhnlein C (2008) Research frontiers in climate change: effects of extreme meteorological events on ecosystems. Comptes Rendus Geosci 340:621–628. https://doi.org/10.1016/j.crte.2008.07.002

    Google Scholar 

  • Julien Y, Sobrino J (2009) Global land surface phenology trends from GIMMS database. Int J Remote Sens 13:3495–3513

    Google Scholar 

  • Katz R, Brown B (1992) Extreme events in a changing climate—variability is more important than averages. Clim Change 21:289–302. https://doi.org/10.1007/BF00139728

    Google Scholar 

  • Kearney M, Porter W (2009) Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol Lett 12:334–350. https://doi.org/10.1111/j.1461-0248.2008.01277.x

    Google Scholar 

  • King A, Black M, Min S, Fischer E, Mitchell D, Harrington L, Perkins-Kirkpatrick S (2016) Emergence of heat extremes attributable to anthropogenic influences. Geophys Res Lett 43:3438–3443. https://doi.org/10.1002/2015GL067448

    Google Scholar 

  • Knutti R, Masson D, Gettelman A (2013) Climate model genealogy: generation CMIP5 and how we got there. Geophys Res Lett 40:1194–1199

    Google Scholar 

  • Koren V, Schaake K, Duan Q, Chen F, Baker J (1999) A parameterization of snowpack and frozen ground intended for NCEP weather and climate models. J Geophys Res 104:19569–19585

    Google Scholar 

  • Latimer C, Zuckerberg B (2019) How extreme is extreme? Demographic approaches inform the occurrence and ecological relevance of extreme events. Ecol Monographs 89:UNSP e01385. https://doi.org/10.1002/ecm.1385

  • Maurer E, Wood A, Adam J, Lettenmaier D, Nijssen B (2002) A long-term hydrologically based dataset of land surface fluxes and states for the conterminous United States. J Clim 15:3237–3251. https://doi.org/10.1175/1520-0442(2002)015⟨3237:ALTHBD⟩2.0.CO;2

    Google Scholar 

  • Maurer E, Brekke L, Pruitt T, Duffy P (2007) Fine-resolution climate projections enhance regional climate change impact studies. Eos Trans AGU 88:504

    Google Scholar 

  • Mora C, Dousset B, ICaldwell I, Powell F, Geronimo R, Bielecki C, Counsell C, Dietrich B, Johnston E, Louis L, Lucas M, McKenzie M, Shea A, Tseng H, Giambelluca T, Leon L, Hawkins E, Trauernicht C (2017) Global risk of deadly heat. Nat Clim Change 7:501–506. https://doi.org/10.1038/nclimate3322

    Google Scholar 

  • Moran M, Peters D, McClaran M, Nichols M, Adams M (2008) Long-term data collection at USDA experimental sites for studies of ecohydrology. Ecohydrology 1:377–393. https://doi.org/10.1002/eco.24

    Google Scholar 

  • Noia Junior R, do Amaral G, Macedo Pezzopane J, Toledo J, Teixeira Xavier T (2018) Ecophysiology of C3 and C4 plants in terms of responses to extreme soil temperatures. Theor Exp Plant Physiol 30:261–274. https://doi.org/10.1007/s40626-018-0120-7

    Google Scholar 

  • O’Sullivan O, Heskel M, Reich P, Tjoelker M, Weerasinghe L, Penillard A, Zhu L, Egerton J, Bloomfield K, Creek D, Bahar N, Griffin K, Hurry V, Meir P, Turnbull M, Atkin O (2017) Thermal limits of leaf metabolism across biomes. Glob Change Biol 23:209–223. https://doi.org/10.1111/gcb.13477

    Google Scholar 

  • Petrie M, Pockman W, Pangle R, Limousin J, Plaut J, McDowell N (2015) Winter climate change promotes altered spring growing season in piñon pine-juniper woodlands. Agric For Meteorol 214–215:357–368. https://doi.org/10.1016/j.agrformet.2015.08.269

    Google Scholar 

  • Petrie M, Bradford J, Hubbard R, Lauenroth W, Andrews C, Schlaepfer D (2017) Climate change may restrict dryland forest regeneration in the 21st century. Ecology 90:1548–1559. https://doi.org/10.1002/ecy.1791

    Google Scholar 

  • Petrie M, Wildeman A, Bradford J, Hubbard R, Lauenroth W (2016) A review of precipitation and temperature control on seedling emergence and establishment for ponderosa and lodgepole pine forest regeneration. For Ecol Manag 361:328–338. https://doi.org/10.1016/j.foreco.2015.11.028

    Google Scholar 

  • Pineiro G, Perelman S, Guerschman J, Paruelo J (2008) How to evaluate models: observed vs. predicted or predicted vs. observed? Ecol Model 216:316–322. https://doi.org/10.1016/j.ecolmodel.2008.05.006

    Google Scholar 

  • Poeplau C, Katterer T, Leblans N, Sigurdsson B (2017) Sensitivity of soil carbon fractions and their specific stabilization mechanisms to extreme soil warming in a subarctic grassland. Glob Change Biol 23:1316–1327. https://doi.org/10.1111/gcb.13491

    Google Scholar 

  • Porter J, Semenov M (2005) Crop responses to climatic variation. Philos Trans R Soc B - Biol Sci 360:2021–2035. https://doi.org/10.1098/rstb.2005.1752

    Google Scholar 

  • Potter K, Woods H, Pincebourde S (2013) Microclimatic challenges in global change biology. Glob Change Biol 19:2932–2939. https://doi.org/10.1111/gcb.12257

    Google Scholar 

  • Qian B, Gregorich E, Gameda S, Hopkins D, Wang X (2011) Observed soil temperature trends associated with climate change in Canada. J Geophys Res - Atmos 116. https://doi.org/10.1029/2010JD015012

  • R Development Core Team (2019) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0, http://www.R-project.org

  • Ratajczak Z, Churchill A, Ladwig L, Taylor J, Collins S (2019) The combined effects of an extreme heatwave and wildfire on tallgrass prairie vegetation. J Veg Sci 30:687–697. https://doi.org/10.1111/jvs.12750

    Google Scholar 

  • Reichstein M, Bahn M, Ciais P, Frank D, Mahecha M, Seneviratne S, Zscheischler J, Beer C, Buchmann N, Frank D, Papale D, Rammig A, Smith P, Thonicke K, van der Velde M, Vicca S, Walz A, Wattenbach M (2013) Climate extremes and the carbon cycle. Nature 500:287–295. https://doi.org/10.1038/nature12350

    Google Scholar 

  • Rodriguez-Caballero E, Belnap J, Buedel B, Crutzen P, Andreae M, Poeschl U, Weber B (2018) Dryland photoautotrophic soil surface communities endangered by global change. Nat Geosci 11:185+. https://doi.org/10.1038/s41561-018-0072-1

    Google Scholar 

  • Rudgers J, Dettweiler-Robinson E, Belnap J, Green L, Sinsabaugh R, Young K, Cort C, Darrouzet-Nardi A (2018) Are fungal networks key to dryland primary production? Am J Bot 105:1783–1787. https://doi.org/10.1002/ajb2.1184

    Google Scholar 

  • Rupp D, Abatzoglou J, Hegewisch K, Mote P (2013) Evaluation of CMIP5 20th century climate simulations for the Pacific Northwest USA. J Geophys Res - Atmospheres 118:10884–10906. https://doi.org/10.1002/jgrd.50843

    Google Scholar 

  • Sarle W (1995) Stopped training and other remedies for overfitting. In: Proceedings of the symposium on the interface of computing science and statistics, vol 27, pp 1–10

  • Schlaepfer D, Andrews C (2019) rSFSW2: simulation framework for SOILWAT2. R package version 3.2.0, https://github.com/DrylandEcology/rSFSW2

  • Schlaepfer D, Murphy R (2019) rSOILWAT2: an ecohydrological ecosystem-scale water balance simulation model. R package version 2.5.0. https://github.com/DrylandEcology/rSOILWAT2

  • Schlaepfer D, Lauenroth W, Bradford J (2012) Consequences of declining snow accumulation for water balance of mid-latitude dry regions. Glob Change Biol 18:1988–1997. https://doi.org/10.1111/j.1365-2486.2012.02642.x

    Google Scholar 

  • Schlaepfer D, Bradford J, Lauenroth W, Munson S, Tietjen B, Hall S, Wilson S, Duniway M, Jia G, Pyke D, Lkhagva A, Jamiyansharav K (2017) Climate change reduces extent of temperate drylands and intensifies drought in deep soils. Nat Commun 8. https://doi.org/10.1038/ncomms14196

  • Selig E, Casey K, Bruno J (2010) New insights into global patterns of ocean temperature anomalies: implications for coral reef health and management. Glob Ecol Biogeogr 19:397–411. https://doi.org/10.1111/j.1466-8238.2009.00522.x

    Google Scholar 

  • Soil Survey Staff (2016) U.S. General Soil Map (STATSGO2). Natural Resources Conservation Service, United States Department of Agriculture, Available online at http://sdmdataaccess.nrcs.usda.gov/. Accessed 2016

  • Svilicic P, Vucetic V, Filic S, Smolic A (2016) Soil temperature regime and vulnerability due to extreme soil temperatures in Croatia. Theor Appl Climatol 126:247–263. https://doi.org/10.1007/s00704-015-1558-z

    Google Scholar 

  • Thuiller W, Albert C, Araujo M, Berry P, Cabeza M, Guisan A, Hickler T, Midgely G, Paterson J, Schurr F, Sykes M, Zimmermann N (2008) Predicting global change impacts on plant species’ distributions: future challenges. Perspect Plant Ecol Evol System 9:137–152. https://doi.org/10.1016/j.ppees.2007.09.004

    Google Scholar 

  • Tian H, Lu C, Ciais P, Michalak A, Canadell J, Saikawa E, Huntzinger D, Gurney K, Sitch S, Zhang B, Yang J, Bousquet P, Bruhwiler L, Chen G, Dlugokencky E, Friedlingstein P, Melillo J, Pan S, Poulter B, Prinn R, Saunois M, Schwalm C, Wofsy S (2016) The terrestrial biosphere as a net source of greenhouse gases to the atmosphere. Nature 531:225+. https://doi.org/10.1038/nature16946

    Google Scholar 

  • Tohver I, Hamlet A, Lee S-Y (2014) Impacts of 21st-century climate change on hydrologic extremes in the Pacific Northwest region of North America. J Am Water Resour Assoc 50:1461–1476

    Google Scholar 

  • Ukkola A, Pitman A, Donat M, Kauwe M, Angelil O (2018) Evaluating the contribution of land-atmosphere coupling to heat extremes in CMIP5 models. Geophys Res Lett 45:9003–9012. https://doi.org/10.1029/2018GL079102

    Google Scholar 

  • Urza A, Weisberg P, Chambers J, Sullivan B (2019) Shrub facilitation of tree establishment varies with ontogenetic stage across environmental gradients. New Phytol 223:1795–1808. https://doi.org/10.1111/nph.15957

    Google Scholar 

  • Wenger S, Olden J (2012) Assessing transferability of ecological models: an underappreciated aspect of statistical validation. Methods Ecol Evol 3:260–267. https://doi.org/10.1111/j.2041-210X.2011.00170.x

    Google Scholar 

  • Wheeler J, Hoch G, Cortes A, Sedlacek J, Wipf S, Rixen C (2014) Increased spring freezing vulnerability for alpine shrubs under early snowmelt. Oecologia 175:219–229. https://doi.org/10.1007/s00442-013-2872-8

    Google Scholar 

  • Whitney K, Vivoni E, Duniway M, Bradford J, Reed S, Belnap J (2017) Ecohydrological role of biological soil crusts across a gradient in levels of development. Ecohydrology 10:e1875. https://doi.org/10.1002/eco.1875

    Google Scholar 

  • Williams AP, Allen CD, Macalady AK, Griffin D, Woodhouse CA, Meko DM, Swetnam TW, Rauscher SA, Seager R, Grissino-Mayer HD, Dean JS, Cook ER, Gangodagamage C, Cai M, McDowell NG (2013) Temperature as a potent driver of regional forest drought stress and tree mortality. Nat Clim Change 3:292–297. https://doi.org/10.1038/NCLIMATE1693

    Google Scholar 

  • Xu B, Arain M, Black T, Law B, Pastorello G, Chu H (2019) Seasonal variability of forest sensitivity to heat and drought stresses: a synthesis based on carbon fluxes from North American forest ecosystems. Glob Change Biol, in press. https://doi.org/10.1111/gcb.14843

  • Yuste J, Baldocchi D, Gershenson A, Goldstein A, Misson L, Wong S (2007) Microbial soil respiration and its dependency on carbon inputs, soil temperature and moisture. Glob Change Biol 13:2018–2035. https://doi.org/10.1111/j.1365-2486.2007.01415.x

    Google Scholar 

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Funding

Portions of this study were funded by a grant from the USDA Forest Service, Western Wildland Environmental Threat Assessment Center.

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Correspondence to M. D. Petrie.

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SOILWAT2 documentation and code is available on github (see Schlaepfer and Andrews 2019 and Schlaepfer and Murphy 2019).

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Appendix

Appendix

We used the peak over threshold (POT) technique of Coelho et al. (2008) to calculate monthly extreme temperature thresholds from daily values of Ta and Ts. This analysis is conducted for a timeseries of each month individually (e.g., all days in January from 1980 to 2015). We calculated the monthly extreme temperature threshold using the following steps:

  1. Step 1.

    Calculate the 5-year floating mean from daily values. Using the 5-year floating mean results in an analysis window that is 4 years shorter than the time period length. For example, 1980–2015 has a floating mean for 1982–2013.

  2. Step 2.

    Rank the daily values in each month (January, 1983, for example) that are above the corresponding floating mean temperature value from highest to lowest (x = a vector of ranked daily temperature values).

  3. Step 3.

    Multiply the number of daily values in x by 0.05 (Obs = number of observations in x multiplied by 0.05).

  4. Step 4.

    If Obs < 1.0, there is no extreme threshold value for the corresponding month, and therefore no extreme event.

  5. Step 5.

    If Obs ≥ 1.0, the extreme threshold value for the corresponding month is the Obs+ 1th value in x. This value is assigned as a monthly extreme event.

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Petrie, M.D., Bradford, J.B., Lauenroth, W.K. et al. Non-analog increases to air, surface, and belowground temperature extreme events due to climate change. Climatic Change 163, 2233–2256 (2020). https://doi.org/10.1007/s10584-020-02944-7

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  • DOI: https://doi.org/10.1007/s10584-020-02944-7

Keywords

  • Soil temperature
  • Air temperature
  • Extremes
  • Ecosystems