the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model and proxy evidence for coordinated changes in the hydroclimate of distant regions over the Last Millennium
Pedro José Roldán-Gómez
Jesús Fidel González-Rouco
Jason E. Smerdon
Félix García-Pereira
Abstract. The Medieval Climate Anomaly (MCA; ca. 950–1250 CE) and the Little Ice Age (LIA; ca. 1450–1850 CE) periods, generally characterised by respectively higher and lower temperatures in many regions, have also been associated with drier and wetter conditions in areas around the Intertropical Convergence Zone (ITCZ), the Asian Monsoon region, and in areas impacted by large-scale climatic modes like the Northern and Southern Annular Modes (NAM and SAM, respectively). To analyse coordinated changes in large-scale hydroclimate patterns, and whether similar changes also extend to other periods of the Last Millennium (LM) outside the MCA and the LIA, reconstruction-based products have been analysed, including the collection of tree-ring based Drought Atlases (DA), the Paleo Hydrodynamics Data Assimilation product (PHYDA) and the Last Millennium Reanalysis (LMR). These analyses have shown coherent changes in the hydroclimate of tropical and extratropical regions, such as northern and central South America, East Africa, western North America, Western Europe, the Middle East, Southeast Asia and the Indo-Pacific, during the MCA, the LIA and other periods of the LM. Comparisons with model simulations from the Community Earth System Model – Last Millennium Ensemble (CESM-LME) and phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) show that both external forcing and internal variability contributed to these changes, with the contribution of internal variability being particularly important in the Indo-Pacific basin and that of external forcing in the Atlantic basin. These results may help to identify not only those areas showing coordinated changes, but also those regions where model simulations are able to successfully reproduce the evolution of hydroclimate during the LM.
Pedro José Roldán-Gómez et al.
Status: open (until 23 Jun 2023)
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CC1: 'Comment on cp-2023-21', Matthew Kirby, 02 May 2023
reply
Might add other coastal sw US sites -
Zaca Lake (Kirby et al., 2014) - Wet LIA, Dry MCA
Lake Elsinore (Kirby et al., 2019) - Wet LIA, Dry MCA
Abbott Lake (Hiner et al., 2016) - Wet LIA, Dry MCA
Maddox Lake (Kirby et al., in press in Quaternary Research) - Wet LIA, Dry MCA
These sites will extend your analysis into the coastal sw US as well as far northwest CA (Maddox Lake)
Citation: https://doi.org/10.5194/cp-2023-21-CC1 -
RC1: 'Comment on cp-2023-21', Anonymous Referee #1, 30 May 2023
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Review of "Model and proxy evidence for coordinated changes in the hydroclimate of distant regions over the Last Millennium" by Roldán-Gómez et al.
The authors present a comprehensive overview of existing hydro-climatic proxies, reconstructions and simulations and draw links to circulations changes such as shifts of the ITCZ. I suggest to publish this study after major revisions. On the one hand, I think the manuscript could be condensed by removing the analysis of temperature to focus more on the hydroclimate. On the other hand, I suggest to add more details: The authors check if reconstructions for different seasons show comparable variability. However, I suggest a more extensive discussion of seasonality issues. Many northern hemisphere proxies/reconstructions may be biased towards JJA, southern hemisphere proxies towards DJF and tropical proxies toward one or two rain seasons. Simulations could give us an idea how much e.g. missing winter precipitation information in the extratropics may influence the results of this study. It is also not clear to me if the proxy collection is independent from the proxies used in the reconstructions?
However, my main concern after seeing the time series in Fig. 7 are possible problems with the PDSI values due to different trends in the simulations, especially in the 20th century. Will the PDSI differences between CESM, CMIP5 and CMIP diminish if PDSI is calculated and calibrated during the preindustrial period? At least the y-axes scales in Fig. 7 suggest that for DAs and PHYDA PDSI varies between positive and negative values while it appears to be mostly negative in the simulations during the pre-industrial period. Does this effect the LIA-MCA differences, too?
In general it would be helpful understand how PDSI calculations based on the simulations could be influenced by model biases of temperature and precipitation on which PDSI is based. Therefore, I would suggest to compare PDSI of all reconstructions and simulations to instrumental observation based PDSI in the 20th century, e.g. the CRU PDSI data set (https://crudata.uea.ac.uk/cru/data/drought/).
Abstract:
L. 1ff, l. 8 ff. Please try to shorten your often really long sentences. Especially with all the references, sentences are often complicated to read on the first pages.L. 12: Please explain how internal variability could cause a 300 to 400 year warm or cold period?
L. 14: Please clarify the last sentence of the abstract.
Introduction:This is a great global collection of proxies. I miss information and discussion on the seasonality of the proxies, which could be added to Tab.1. Could seasonality explain part of the differences between locations?
Fig. 1: All arrows are difficult to understand in the map. Additionally, all codes can only be well understood after scrolling two pages down to Tab. 2. Therefore, Fig.1 and Tab.2 should be right next to each other. Maybe better use the abbreviations like NAO, ITCZ, etc. directly in the map directly instead of A1, B4, ...
Data and methods:How could the much small number of proxies for the MCA compared to the LIA influence all proxy-based data sets and therefore the results of this study?
Are all data sets and simulations going back to 950 C.E.? In Tab. 3 I see the simulations of Tett et al. 2007 listed, which do not go that far back in time as fr as I know. The MXDA (Stahle et al. 2016) is also does not cover the MCA and you also do not show it in Fig. 2. Which DAs are really included in all analysis including the correlation analysis in Tab. 4?
L. 108: You say that you focus on the JJA season. Is the analysis only done for the northern hemisphere? Because in the southern hemisphere the reconstructions are possible representing rather the DJF season.
3.1 ReconstructionsFig. 2: Surprisingly large disagreement between drought atlases and PHYDA even in the sign of change for western half of Europe and parts of North America with fewer proxies. This should be mentioned in the results more clearly. Is this a result of the different methods or the underlying data? For the proxy locations indicated by the dots it is unclear to me, which are independent/additional information and which are already included in which of the gridded reconstructions and therefore may indicate better agreement.
Fig. 3 and 5: Does it even make sense to present temperature reconstructions or even the temperature focussed LMR data set in this study on hydroclimate? I would suggest to remove these figures and related text or move it to the appendix.
3.2 Simulated hydroclimate
Yes, model ensembles should let us understand the forced signal. However, there is surprisingly large disagreement between model generations/sets. How different are CMIP5/6 PMIP3/4 forcings averaged of MCA and LIA? Or is the reason rather that different models are in both sets?
L. 155: I would not call this a good agreement over western North America. In contrast to the reconstruction especially the simulations CESM-LME show a continent wide uniformly more moist LIA in North America and Europe.
3.3. AgreementL. 182: You mention the role of internal variability. Can you explain why multi-century averages in the case of reconstructions should still contain internal variability? Can this be shown in control simulations?
You mention the role of the ITCZ for the Indo-Pacific region but why is the disagreement large in the rather data rich regions of western North America and western Europe?
Fig. 6: I generally like the idea of showing the signal in all products together. However, this seem to be a rather subjective weighting of the data sets. Why is LMR included although it is not really good in reconstructing drought? Why are state-of-art CMIP6 simulations weighted the same as older simulations? In b) there should be 8 CMIP5 and only 3 CMIP simulations according to Tab.3. Is the signal between CMIP5 and CMIP6 simulations just different because we look at different models or because of newer model versions or because of different forcings? How sensitive are all results to changes in the weighting?
Fig. 7: Now for Pakistan is becomes clear that there in no DA data for the MCA. I guess this just needs to be clarified in the data and methods section that MADA is only used in this part of the study and not for the LIA minus MCA comparison.
Surprising how small the agreement between DAs and PHYDA is outside of western North America although they are probably based on similar tree-ring collections. This could be mentioned more prominently because it is probably worth knowing for many people working in the field.
Correlations are probably affected by 20th century trend in some simulations. I suggest calculating PDSI in the pre-industrial period and showing correlations for this period, too. This may effect your discussion on the role of internal vs external variability as well.
Concerning the role of internal vs external variability, I would also appreciate a discussion of how the data sparse region with less reconstruction skill can have an influence.
3.4. Correlation between distant regionsHow much of the teleconnections discussed here are really constrained by the proxy data? The reconstructions methods use a stationary covariance structure of the underlying model simulations and in some of the regions discussed here, there is very little data assimilated. Are similar teleconnections already existing in the simulation ensembles? In this regard, it would be helpful to see the location where data has been assimilated into PHYDA/LMR already since the beginning of the MCA.
I would also not show the LMR as it did not seen to do a great job as the drought reconstruction. Rather show the teleconnections in the simulations, especially CESM with underlies PHYDA if I remember correctly.
Citation: https://doi.org/10.5194/cp-2023-21-RC1
Pedro José Roldán-Gómez et al.
Pedro José Roldán-Gómez et al.
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