Effect of climate data on simulated carbon and nitrogen balances for Europe

Journal of Geophysical Research: Biogeosciences

Volumn 121, Issue 5, 2016, Pages 1352-1371

  Blanke, Jan Hendrik ^a   Lindeskog, Mats ^a   Lindström, Johan ^b   Lehsten, Veiko ^a  

^a LUND UNIVERSITY   (Sweden)

^b LUND UNIVERSITY   (Sweden)

Author keywords

climate data; DGVM; downscaling; LPJ GUESS; sensitivity; spatial resolution

Indexed keywords

CARBON BALANCE; CARBON CYCLE; CARBON FLUX; CONCENTRATION (COMPOSITION); DATA SET; DOWNSCALING; FACTOR ANALYSIS; NITROGEN CYCLE; SPATIAL RESOLUTION; SPATIAL VARIATION;

ALPS; IBERIAN PENINSULA; SCANDINAVIA;

EID: 84971229248     PISSN: 21698953     EISSN: 21698961     Source Type: Journal    
DOI: 10.1002/2015JG003216     Document Type: Article

References (85)

1. Ahlström, A., G. Schurgers, A. Arneth, and B. Smith (2012), Robustness and uncertainty in terrestrial ecosystem carbon response to CMIP5 climate change projections, Environ. Res. Lett., 7(4), 044008.
2. Ahlström, A., B. Smith, J. Lindström, M. Rummukainen, and C. B. Uvo (2013), GCM characteristics explain the majority of uncertainty in projected 21st century terrestrial ecosystem carbon balance, Biogeosci., 10(3), 1517–1528.
3. Archer, G. E. B., A. Saltelli, and I. M. Sobol (1997), Sensitivity measures, ANOVA-like techniques and the use of bootstrap, J. Stat. Comput. Simul., 58, 99–120.
4. Arneth, A., et al. (2010), Terrestrial biogeochemical feedbacks in the climate system, Nat. Geosci., 3, 525–532.
5. Benestad, R. E. (2002), Empirically downscaled temperature scenarios for northern Europe based on a multi-model ensemble, Clim. Res., 21(1991), 105–125.
6. Berthelot, M., P. Friedlingstein, P. Ciais, J.-L. Dufresne, and P. Monfray (2005), How uncertainties in future climate change predictions translate into future terrestrial carbon fluxes, Global Change Biol., 11(6), 959–970.
7. Bonan, G. B. (2008), Forests and climate change: Forcings, feedbacks, and the climate benefits of forests, Science, 320(5882), 1444–1449.
8. Bondeau, A., et al. (2007), Modelling the role of agriculture for the 20th century global terrestrial carbon balance, Global Change Biol., 13(3), 679–706.
9. Bugmann, H. (2001), A review of forest gap models, Clim. Change, 51, 259–305.
10. Caya, D., and R. Laprise (1999), A semi-implicit semi-Lagrangian regional climate model: The Canadian RCM, Mon. Weather Rev., 127, 341–362.
11. Cosby, B. J., G. M. Hornberger, R. B. Clapp, and T. R. Ginn (1984), A statistical exploration of the relationships of soil moisture characteristics to the physical properties of soils, Water Resour. Res., 20(6), 682–690.
12. 2 and climate change: Results from six dynamic global vegetation models, Global Change Biol., 7, 357–373.
13. Christensen, J. H., T. R. Carter, M. Rummukainen, and G. Amanatidis (2007), Evaluating the performance and utility of regional climate models: The PRUDENCE project, Clim. Change, 81, 1–6.
14. 2 enrichment sites, New Phytol., 203, 883–899.
15. Eden, J. M., and M. Widmann (2014), Downscaling of GCM-simulated precipitation using model output statistics, J. Clim., 27, 312–324.
16. Ehret, U., E. Zehe, V. Wulfmeyer, K. Warrach-Sagi, and J. Liebert (2012), HESS Opinions “should we apply bias correction to global and regional climate model data?”, Hydrol. Earth Syst. Sci., 16(9), 3391–3404.
17. Friedlingstein, P., et al. (2006), Climate-carbon cycle feedback analysis: Results from the c4mip model intercomparison, J. Clim., 19, 3337–3353.
18. Fronzek, S., T. R. Carter, and K. Jylhij (2012), Representing two centuries of past and future climate for assessing risks to biodiversity in Europe, Global Ecol. Biogeogr., 21, 19–35.
19. Gerten, D., S. Schaphoff, U. Haberlandt, W. Lucht, and S. Sitch (2004), Terrestrial vegetation and water balance—Hydrological evaluation of a dynamic global vegetation model, J. Hydrol., 286(1–4), 249–270.
20. Giorgi, F., C. Jones, and G. R. Asrar (2009), Addressing climate information needs at the regional level: The CORDEX framework, Bull. World Meteorol. Organ., 58, 175–183.
21. Haxeltine, A., and I. C. Prentice (1996), Biome3: An equilibrium terrestrial biosphere model based on ecophysiological constraints, resource availability, and competition among plant functional types, Global Biogeochem. Cycles, 10(4), 693–709.
22. Hickler, T., I. C. Prentice, B. Smith, M. T. Sykes, and S. Zaehle (2006), Implementing plant hydraulic architecture within the LPJ dynamic global vegetation model, Global Ecol. Biogeogr., 15(6), 567–577.
23. 2 fertilization in temperate FACE experiments not representative of boreal and tropical forests, Global Change Biol., 14, 1531–1542.
24. Hickler, T., et al. (2012), Projecting the future distribution of european potential natural vegetation zones with a generalized, tree species-based dynamic vegetation model, Global Ecol. Biogeogr., 21(1), 50–63.
25. Jacob, D., et al. (2014), EURO-CORDEX: New high-resolution climate change projections for European impact research, Reg. Environ. Change, 14, 563–578.
26. Jönsson, A. M., F. Lagergren, and B. Smith (2015), Forest Management facing Climate Change—An Ecosystem Model Analysis of Adaptation Strategies, Mitigation and Adaptation Strategies for Global Change, 20(2), 201–220.
27. Juneng, L., F. T. Tangang, H. Kang, W. J. Lee, and Y. K. Seng (2010), Statistical downscaling forecasts for winter monsoon precipitation in Malaysia using multimodel output variables, J. Clim., 23, 17–27.
28. Jung, M., G. Le Maire, S. Zaehle, S. Luyssaert, M. Vetter, G. Churkina, P. Ciais, N. Viovy, and M. Reichstein (2007a), Assessing the ability of three land ecosystem models to simulate gross carbon uptake of forests from boreal to Mediterranean climate in Europe, Biogeosciences, 4, 647–656.
29. Jung, M., et al. (2007b), Uncertainties of modeling gross primary productivity over Europe: A systematic study on the effects of using different drivers and terrestrial biosphere models, Global Biogeochem. Cycles, 21, GB4021, doi:10.1029/2006GB002915.
30. Kimball, J. S., S. W. Running, and S. S. Saatchi (1999), Sensitivity of boreal forest regional water flux and net primary production simulations to sub-grid-scale land cover complexity, J. Geophys. Res., 104(D22), 27789.
31. Lamarque, J.-F., et al. (2013), Multi-model mean nitrogen and sulfur deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): Evaluation of historical and projected future changes, Atmos. Chem. Phys., 13(16), 7997–8018.
32. 2 on projected future European habitat types, Global Ecol. Biogeogr., 24(6), 653–663.
33. Lindeskog, M., A. Arneth, A. Bondeau, K. Waha, J. Seaquist, S. Olin, and B. Smith (2013), Implications of accounting for land use in simulations of ecosystem carbon cycling in Africa, Earth Syst. Dyn., 4, 385–407.
34. Lindstedt, D., P. Lind, E. Kjellstr⊙m, and C. Jones (2015), A new regional climate model operating at the meso-gamma scale: Performance over Europe, Tellus, Ser. A, 67, 1–23.
35. Lindström, J., A. Ahlström, and E. Blom (2011), Using PCA and Global Smoothing to Explore Differences between Global Vegetation Models, 58th World Statistics Congress of the International Statistical Institute, Dublin, Ireland.
36. Maraun, D., et al. (2010), Precipitation downscaling under climate change: Recent developements to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, 1–38.
37. Mearns, L. O., F. Giorgi, L. McDaniel, and C. Shields (2003), Climate scenarios for the southeastern US based on GCM and regional model simulations, Clim. Change, 60, 7–35.
38. Medvigy, D., S. C. Wofsy, J. W. Munger, and P. R. Moorcroft (2010), Responses of terrestrial ecosystems and carbon budgets to current and future environmental variability, Proc. Natl. Acad. Sci. U.S.A., 107, 8275–8280.
39. Minville, M., F. Brissette, S. Krau, and R. Leconte (2009), Adaptation to climate change in the management of a canadian water-resources system exploited for hydropower, Water Resour. Manage., 23(14), 2965–2986.
40. Mitchell, T. D., and P. D. Jones (2005), An improved method of constructing a database of monthly climate observations and associated high-resolution grids, Int. J. Climatol., 25(6), 693–712.
41. Mitchell, T. D., T. R. Carter, P. D. Jones, M. Hulme, and M. New (2004), A comprehensive set of high-resolution grids of monthly climate for Europe and the globe: The observed record (1901–2000) and 16 scenarios (2001–2100), Tyndall Working Paper 55, 1–30, UEA, Norwich, U. K.
42. Morales, P., T. Hickler, D. P. Rowell, B. Smith, and M. T. Sykes (2007), Changes in European ecosystem productivity and carbon balance driven by regional climate model output, Global Change Biol., 13(1), 108–122.
43. Morales, P., et al. (2005), Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes, Global Change Biol., 11(12), 2211–2233.
44. New, M., M. Hulme, and P. Jones (1999), Representing twentieth-century space-time climate variability. Part I: Development of a 1961–90 mean monthly terrestrial climatology, J. Clim., 12, 829–856.
45. New, M., M. Hulme, and P. Jones (2000), Representing twentieth-century space-time climate variability. Part II: Development of 1901–96 monthly grids of terrestrial surface climate, J. Clim., 12, 2217–2238.
46. O'Brien, K., L. Sygna, and J. E. Haugen (2004), Vulnerable or resilient? A multi-scale assessment of climate impacts and vulnerability in Norway, Clim. Change, 64(1/2), 193–225.
47. Olesen, J. E., et al. (2007), Uncertainties in projected impacts of climate change on European agriculture and terrestrial ecosystems based on scenarios from regional climate models, Clim. Change, 81, 123–143.
48. 2 and N management in the main wheat regions of western Europe, Biogeosciences, 12(8), 2489–2515.
49. Olin, S., M. Lindeskog, T. A. M. Pugh, G. Schurgers, D. Wårlind, M. Mishurov, S. Zaehle, B. D. Stocker, B. Smith, and A. Arneth (2015), Soil carbon management in large-scale Earth system modelling: Implications for crop yields and nitrogen leaching, Earth Syst. Dyn. Discuss., 6, 1047–1100.
50. Pappas, C., S. Fatichi, S. Leuzinger, A. Wolf, and P. Burlando (2013), Sensitivity analysis of a process-based ecosystem model: Pinpointing parameterization and structural issues, J. Geophys. Res. Biogeosci., 118, 505–528, doi:10.1002/jgrg.20035.
51. Pappas, C., S. Fatichi, S. Rimkus, P. Burlando, and M. Huber (2015), The role of local scale heterogeneities in terrestrial ecosystem modeling, J. Geophys. Res. Biogeosci., 120, 341–360.
52. Parmesan, C. (2006), Ecological and evolutionary responses to recent climate change, Annu. Rev. Ecol. Evol. Syst., 37, 637–669.
53. Perpiñán, O., and R. Hijmans (2014), rasterVis, R Package Version 0.32. [Available at https://cran.r-project.org/web/packages/rasterVis/citation.html.]
54. 2 trends, Global Change Biol., 19(7), 2117–2132.
55. Potter, K. a., H. Arthur Woods, and S. Pincebourde (2013), Microclimatic challenges in global change biology, Global Change Biol., 19, 2932–2939.
56. Prentice, I. C., M. T. Sykes, and W. Cramer (1993), A simulation model for the transient effects of climate change on forest landscapes, Ecol. Modell., 65(1–2), 51–70.
57. R Core Team (2015), R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria.
58. Saltelli, A., P. Annoni, I. Azzini, F. Campolongo, M. Ratto, and S. Tarantola (2010), Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Comput. Phys. Commun., 181, 259–270.
59. Schaphoff, S., W. Lucht, D. Gerten, S. Sitch, W. Cramer, and I. C. Prentice (2006), Terrestrial biosphere carbon storage under alternative climate projections, Clim. Change, 74(1–3), 97–122.
60. Scholze, M., W. Knorr, N. W. Arnell, and I. C. Prentice (2006), A climate-change risk analysis for world ecosystems, Proc. Natl. Acad. Sci. U.S.A., 103(35), 13,116–13,120.
61. Schröter, D., et al. (2005), Ecosystem service supply and vulnerability to global change in Europe, Science, 310(5752), 1333–1337.
62. Sears, M. W., E. Raskin, and M. J. Angilletta (2011), The world is not Flat: Defining relevant thermal landscapes in the context of climate change, Integr. Comp. Biol., 51, 666–675.
63. Sitch, S., et al. (2003), Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Global Change Biol., 9(2), 161–185.
64. Sitch, S., et al. (2008), Evaluation of the terrestrial carbon cycle, future plant geography and climate-carbon cycle feedbacks using five dynamic global vegetation models (DGVMs), Global Change Biol., 14, 2015–2039.
65. Sitch, S., et al. (2015), Recent trends and drivers of regional sources and sinks of carbon dioxide, Biogeosciences, 12(3), 653–679.
66. Smith, B., I. C. Prentice, and M. T. Sykes (2001), Representation of vegetation dynamics in the modelling of terrestrial ecosystems: Comparing two contrasting approaches within European climate space, Global Ecol. Biogeogr., 10(6), 621–637.
67. Smith, B., W. Knorr, J.-L. Widlowski, B. Pinty, and N. Gobron (2008), Combining remote sensing data with process modelling to monitor boreal conifer forest carbon balances, For. Ecol. Manage., 255(12), 3985–3994.
68. Smith, B., P. Samuelsson, A. Wramneby, and M. Rummukainen (2011), A model of the coupled dynamics of climate, vegetation and terrestrial ecosystem biogeochemistry for regional applications, Tellus, Ser. A, 63(1), 87–106.
69. Smith, B., D. Wårlind, A. Arneth, T. Hickler, P. Leadley, J. Siltberg, and S. Zaehle (2014), Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model, Biogeosciences, 11, 2027–2054.
70. Tang, G., B. Beckage, B. Smith, and P. A. Miller (2010), Estimating potential forest NPP, biomass and their climatic sensitivity in New England using a dynamic ecosystem model, Ecosphere, 1(6), 1–20.
71. Tang, G., B. Beckage, and B. Smith (2012), The potential transient dynamics of forests in New England under historical and projected future climate change, Clim. Change, 114, 357–377.
72. Taylor, K. E., R. J. Stouffer, and G. A. Meehl (2012), An overview of CMIP5 and the experiment design, Bull. Am. Meteorol. Soc., 93, 485–498.
73. Teutschbein, C., and J. Seibert (2013), Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions, Hydrol. Earth Syst. Sci., 17(12), 5061–5077.
74. Turner, D. P., W. B. Cohen, and R. E. Kennedy (2000), Alternative spatial resolutions and estimation of carbon flux over a managed forest landscape in western Oregon, Landscape Ecol., 15(5), 441–452.
75. van Vuuren, D. P., et al. (2011), The representative concentration pathways: An overview, Clim. Change, 109, 5–31.
76. Wickham, H. (2009), GGPLOT2: Elegant Graphics for Data Analysis, Springer, New York.
77. Wilby, R. L., J. Troni, Y. Biot, L. Tedd, B. C. Hewitson, D. M. Smith, and R. T. Sutton (2009), A review of climate risk information for adaptation and development planning, Int. J. Clim., 29(9), 1193–1215.
78. Wilks, D. S. (2010), Use of stochastic weathergenerators for precipitation downscaling, Wiley Interdisciplin. Rev. Clim. Change, 1, 898–907.
79. Wood, E. F., et al. (2011), Hyperresolution global land surface modeling: Meeting a grand challenge for monitoring Earth's terrestrial water, Water Resour. Res., 47, W05301, doi:10.1029/2010WR010090.
80. Wramneby, A., B. Smith, and M. T. Sykes (2008), Parameter uncertainties in the modelling of vegetation dynamics Effects on tree community structure and ecosystem functioning in European forest biomes, Ecol. Modell., 216, 277–290.
81. Zaehle, S., S. Sitch, B. Smith, and F. Hatterman (2005), Effects of parameter uncertainties on the modeling of terrestrial biosphere dynamics, Global Biogeochem. Cycles, 19, GB3020, doi:10.1029/2004GB002395.
82. Zaehle, S., S. Sitch, I. C. Prentice, J. Liski, W. Cramer, M. Erhard, T. Hickler, and B. Smith (2006), The importance of age-related decline in forest NPP for modeling regional carbon balances, Ecol. Appl., 16(4), 1555–1574.
83. Zaehle, S., et al. (2007), Projected changes in terrestrial carbon storage in Europe under climate and land-use change, 1990–2100, Ecosystems, 10(3), 380–401.
84. Zhao, G., S. Siebert, A. Enders, E. E. Rezaei, C. Yan, and F. Ewert (2015), Demand for multi-scale weather data for regional crop modeling, Agric. For. Meteorol., 200, 156–171.
85. Zhao, S., and S. Liu (2014), Scale criticality in estimating ecosystem carbon dynamics, Global Change Biol., 20(7), 2240–2251.