Description and Evaluation of the JULES-ES setup for ISIMIP2b
- 1Met Office Hadley Centre, FitzRoy Road, Exeter, UK
- 2School of Earth and Environment, Institute for Climate and Atmospheric Science, University of Leeds, Leeds, UK
- 3Global Systems Institute, University of Exeter, Laver Building, North Park Road, Exeter, UK
- 4UK Centre for Ecology and Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
- 1Met Office Hadley Centre, FitzRoy Road, Exeter, UK
- 2School of Earth and Environment, Institute for Climate and Atmospheric Science, University of Leeds, Leeds, UK
- 3Global Systems Institute, University of Exeter, Laver Building, North Park Road, Exeter, UK
- 4UK Centre for Ecology and Hydrology, Wallingford, Oxfordshire, OX10 8BB, UK
Abstract. Global studies of climate change impacts that use future climate model projections also require projections of land surface changes. Simulated land surface performance in Earth System models is often affected by the atmospheric models’ climate biases, leading to errors in land surface projections. Here we run the JULES-ES land surface model with ISIMIP2b bias-corrected climate model data from 4 global climate models (GCMs). The bias correction reduces the impact of the climate biases present in individual models. We evaluate JULES-ES performance against present-day observations to demonstrate its usefulness for providing required information for impacts such as fire and river flow. We simulate a historical and two future scenarios; a mitigation scenario RCP2.6 and RCP6.0, which has very little mitigation. We include a standard JULES-ES configuration without fire as a contribution to ISIMIP2b and JULES-ES with fire as a potential future development. Simulations for gross primary productivity (GPP), evapotranspiration (ET) and albedo compare well against observations. Including fire improves the simulations, especially for ET and albedo and vegetation distribution, with some degradation in shrub cover and river flow. This configuration represents some of the most current earth system science for land surface modelling. The suite associated with this configuration provides a basis for past and future phases of ISIMIP, providing a simulation setup, postprocessing and initial evaluation using ILAMB. This suite ensures that it is as straightforward, reproducible and transparent as possible to follow the protocols and participate fully in ISIMIP using JULES.
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Camilla Mathison et al.
Status: final response (author comments only)
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RC1: 'Comment on egusphere-2022-1196', Anonymous Referee #1, 24 Jan 2023
The authors provide a description of the JULES-ES model used as part of the ISIMIP project. The paper is overall well -written and easy to follow, with a fairly detailed description of key model processes and the model set-up. The authors also present an evaluation of present-day model performance to indicate its skill in simulating key ecosystem and water cycle processes. It was particularly great to see the authors using the ILAMB evaluation tool for this purpose as it forms a useful basis for benchmarking any future JULES developments against JULES-ES in a transparent way. Overall, I do not have any major concerns about the manuscript and recommend it for publication subject to some minor revisions.
Specific comments:
L12: Best to spell out what ISIMIP2b stands for (a few other abbreviations in the abstract -GCMs and GPP- are not used and thus not needed)
L30: what do you mean by “representing impacts”?
L39: Intrigued to know why JULES-ES is more efficient than the UKESM version? What are the main differences?
L40: I think you mean uncertainty in climate forcing and its impact on land? Otherwise I don’t quite follow the “land surface forcing” terminology as you only use one LSM
L61: should this say “lower” carbon assimilation rates?
L65: C4 grasses twice
L82: would be good to briefly discuss how realistic it is to assume identical physiology?
L109: Do you mean the TRIFFID time stepping?
L133: Suggest rewording “they are consistent” because it implies that the different ISIMIP2b scenarios are consistent with a >2deg world but this is not true for RCP6.0. I thus found the sentence a little confusing
L136: historical ends in 2005?
L142: should be post-2005?
L141-2: this reads like only the period 1979-2013 was bias-corrected?
L143: Reference should be in brackets
L145: Need to mention all variables that are bias-corrected, this suggests only humidity etc. were
L148-9: Again this reads like only the reference period was corrected, is this correct? Also a bit confused by each GCM having a “different variability and climate” outside of reference period, would this not also be true during the reference period? Perhaps consider rewording some of this section as I find it challenging to follow
L163: check grammar
L164: historic -> historical
L170: could you very briefly explain what a suite is for those not familiar with JULES
L179: Please explain what TRIP is
Figure 1: suggest reverting colours, red normally implies too little water
L193: Also other southeast Asian catchments?
L193: I don’t quite follow how water extractions “disproportionally affecting groundwater depletion” leads to high runoff biases? Also what about other model processes that might be missing, such as river channel evaporation and transmission losses?
Figure S2: needs units
Figure S3: monthly precipitation? And are the units mm/month?
Figure S4: the panels and legend are far too small to read, and the righthand panels are cut off.
L205: is the ET bias shown anywhere? If so, refer to a figure
L216: off -> of
L230: typo
L230-232: Any thoughts on what might be causing these albedo biases?
L238-9: check sentence and reword
L240: global bias in shrub?
L247: Perhaps worth mentioning here again that fire was only turned on in a subset of simulations, and not in the official ISIMIP submissions?
L255: any idea why you see these biases?
L263: spatial distribution of what?
L267: GPPs -> GPP’s
L268: would be useful to know compared to what observed value?
L270: models’ -> model’s
L274: Can you briefly state what these well-documented biases are?
L284: do you mean the seasonal cycle?
L298: results -> this results
L300: none -> non
L305: a bit of a confusing statement (number of trees may lead to higher or lower albedo)
L313: fix brackets
L317: which areas and impacts where?
L332: even for hydrological applications with Q simulations degraded?
L560 and L565: I think you provided a different reference elsewhere in the text?
L561: why weren’t longer observations used where available given the GCMs have their own IAV, making a longer evaluation period preferable?
Figure3: this would be easier to read if the “observed” legends were directly below those panels. Also check albedo unit (“unknown”). Also best to avoid red-green colour schemes
L574: datsets -> datasets
Figure 5: No need to repeat legend 5 times? Also would be helpful if the model maps were shown as biases from obs as in previous figures.
L588: Do these show the ensemble mean?
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RC2: 'Comment on egusphere-2022-1196', Anonymous Referee #2, 24 Jan 2023
General comment:
The paper "Description and Evaluation of the JULES-ES setup for ISIMIP2b" describe the setup and performance of the Jules-ES for the ISMIP2b protocol very well. The paper describe in concise steps the setup of the Jules-ES for the ISIMIP2 simulation protocol and presents the comparison to selected observation (runoff, GPP, ET, albedo) for present day comparison very well.Improvements in the description would be helpful (e.g. how are LU changes tracked within Jules-ES, more info about crop and pasture handling, see specific comments below).
It does fall short on the premise to "describes and evaluates a new modelling methodology to quantify the impacts of climate change on water, biomes and the carbon cycle" and "We simulate a historical and two future scenarios; a mitigation scenario RCP2.6 and RCP6.0, which has very little mitigation. " The paper currently only compares present day conditions to observations, the behavior of Jules-ES for the two future scenarios (impacted by the two contrasting climate change scenarios) are not presented. It would help the reader to judge the performance of Jules-ES in capturing climate change induced changes, if the authors would add a section about the future behavior of the Jules-ES for the contrasting climate change scenarios (e.g. does Jules-ES show an expansion of forest northward in a warmer climate scenario (RCP6.0) compared to a mitigation scenario (RCP2.6)).
I will be happy to review changes to address the above mentioned concern and the following comments for specific sections.
Specific comments:
Abstract: "The bias correction reduces the impact of the climate biases present in individual models. We evaluate JULES-ES performance against present-day observations to demonstrate its usefulness for providing required information for impacts such as fire and river flow. "
Very good to see (Figure 1 and S2) that the still quite large difference between GCMS in bias corrected average annual precipitation in the Amazon region is retained very well in the Jules-ES in the modelled catchment scale runoff.
An open question comes to mind, if not bias corrected GCM output would be used to drive Jules-ES, would that result in a poor performance compared to observations or might it show similar results.Line 38: How more 'computationally efficient' is it, please provide an estimate. e.g. 10% faster or N times faster.
Line 39-41: please clarify, not totally clear what is meant with this.
"while using multi-model climate ensembles sample scientific uncertainty in land surface forcing that would not be possible within a single climate model framework."
Line 64-66:
"tropical broadleaf evergreen trees (BET-Tr)" how is the effect of seasonal drought occurring in various tropical regions handled in the BET-Tr modelling?Line 65-66: "C4 grasses, C4 grasses" one of them is a C3, right?
Line 70-72:
How is mineral and manure nitrogen fertilization handled in the crop modelling? Are there C3 and C4 pasture PFTs? Please provide the crop and pasture PFTs short names (e.g. C3-Cr?).Line 77: "(urban, ice, lakes)", what about ocean fraction?
Line 79-80:
"Conversely, when crop and pasture areas are reduced, the natural PFTs are allowed to recolonize the vacated grid box fraction. " Does 'recolonize' entail that new vegetation individuals establish in the abandoned fractions within the grid box or is the potentially existing Natural fraction just increased?Please describe whether Jules-ES performs net or gross LUC transitions.
The mentioned "After accounting for land-use, the fractional coverage and biomass of each PFT within a grid box is determined by the TRIFFID dynamic vegetation model. " is not clear, whether the landuse history might be tracked within Jules-ES. Are the various LUC induced fractional areas within the grid box tracked separately within the Jules-ES or the existing landuse fractions just adjusted in size (increases and decreases)?Line 81-82: How is grazed or managed pasture grass handled? Any pasture harvest or grazing estimated (e.g. a fraction of grass biomass removed and a portion of the N within the removed biomass returned to the pasture)?
Line 88-90:
"Inputs to the land surface are via biological fixation, fertilization and nitrogen deposition, with losses from the land surface occurring via leaching and gas loss, with Nitrogen deposition being externally provided to the model." How is N fertilization forcing handled?Line 90-91:
"JULES simulates a nitrogen-limited ecosystem by reducing the net primary productivity if there is insufficient available N to satisfy plant N demand. Any excess carbon is added to the plant respiration. " N limitation would lead to reduced assimilation, not directly reducing NPP. What excess carbon? Please clarify.Line 100-101:
"prescribed population density from HYDE3.2 (Goldewijk et al., 2017) gives human ignitions, and prescribed lightning from LIS/OTD version 2.3.2015 (Cecil, 2006) gives natural ignitions." Those seem to be historic data, what about future scenarios? Is present day population and lightning just recycled into the future?Line 105: "C3 and C4 crop PFTs ", what about pasture PFTs handling in INFERNO?
Line 117: "model timestep of 1 hour" In supplement the disaggregator was shown with 3-hour timestep. Please clarify that time step discrepancy.
Line 122: "6h for convective rainfall, 1h for large-scale rainfall" How is convective and large-scale rainfall distinguished?
Line 129 "See Figure S1 for plots showing that using the disaggregator has little effect on vegetation that we would expect to be 130 influenced by rainfall." Figure S1 only shows the BET-Tr tree fraction, which is not a sufficient parameter to judge the effect of the time disaggregation. What about biomass, NPP, ET, soil C? Please add the plots and map differences for those important parameters (at least add the plots for your chosen parameters GPP, ET, albedo).
Line 136: "(1860-2006) and the RCP2.6 and RCP6.0 future concentration pathways (2006-2099)" The switch between historical and future was 2005 in CMIP5, please clarify?
Line 170-171:
"JULES-ES in the form of the suite u-cc669 available via the Met Office Science Repository Service (MOSRS - https://code.metoffice.gov.uk/trac/roses-u see data availability section for information). " MORS requires registration, what about the GMD code and data policy (https://www.geoscientific-model-development.net/policies/code_and_data_policy.html#item4) and its suggested archive of the source code on Zenodo?Line 185-188:
"By assuming there are no losses from the river, we calculated the long-term mean, basin
averaged runoff by dividing the river flow at the river mouth by the basin area." How good is that 'no losses' assumption, considering that in many regions river water is used for irrigation, and industrial and household purposes (https://www.globalagriculture.org/report-topics/water.html).Supplement
Figure S1: Please add a difference map (e.g. b-a). Why would tree fraction be a good estimator to show the climate disaggregation influence? What about other variables biomass, GPP (or NPP), ET, albedo?Technical corrections:
Table S2 caption typos:
"global runoff verus Dai" correct: versus.
"scores indict whether" correct: indicateFigure S4 please correct the clipping of the ET images on in all 4 panels. The 'Observed albedo' z-scale values overlap and are not readable in the 4 panels.
Camilla Mathison et al.
Camilla Mathison et al.
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