Climate change is projected to impact water resources in many countries around the world, but the projections are highly uncertain due to numerous assumptions of the hydrological stationarity, model structures, and complex hydrodynamics in the surface and subsurface. Quantifying the historic impact of climate variability and change on water resources allows for an improved understanding of the hydrological and climate processes which is necessary for accurate projections. Due to the long memory in groundwater systems of the impacts of climate variability and change, there is an opportunity to investigate the historic impact of long-term changes on water resources. Analysing groundwater hydrographs over multiple decades potentially allows for the quantification of the response of groundwater head to climatic changes. However, there are challenges in using this long-term information to quantify historic climate impacts. One of the challenges is to separate the impact of climatic change on groundwater from other influential drivers, such as pumping for agricultural irrigation, land use and land cover changes, and natural climate variability. In addition, the often short and interrupted nature of groundwater records limits the investigation of long-term impacts. In this study, we establish and test methods to quantify the response of groundwater to climate variability and change at natural sites (not affected by anthropogenic activities) identified across Australia, overcoming the aforementioned challenges. Results show that location, climate, and aquifer hydraulic property play a role in controlling the response of groundwater head and recharge to climate variations, compared with land use changes. This implies that future climate change may significantly impact groundwater availability by altering the response of groundwater. Quantifying the response of groundwater to climatic changes is needed to understand the future of groundwater systems globally. With this improved understanding we can work towards effective adaptive water management strategies for both human and natural systems.
Input data for the reconstructions and required datasets and code to generate the figures in the paper. Proxy data used for the reconstructions: proxy_ama_2.0.0_PAGES-crit-regional+FDR.txt: PAGES2k v2.0.0 proxy records, R-FDR screened subset (See PAGES2k Consortium, 2017, Scientific Data, doi: 10.1038/sdata.2017.88). Data are tab separated, the first column is the "paleoData_TSid" to identify each record in the metadata file. First row is year CE. metadata_2.0.0_PAGES-crit-regional+FDR.txt: According metadatam tab separated. Contains a selection of PAGES2k v.2.0.0 database fields in each row: dataSetName, geo_latitude, geo_longitude, archiveType, resMed (Median resolution in years), paleoData_TSid (the column header in the data files). proxy_ama_2.0.0_HR-0.67_infilled_DINEOF_1850-2000_PAGES-crit-regional+FDR.txt: high-resolution (annual and higher) and infilled (calibration period) subset of the proxy data used for the PCR and CPS methods (210 records), see Methods section.
Instrumental target: had4_krig_ama_v2_0_0.txt: April to March aggregated target used for the reconstructions. Space separated, no column headers. Newest updated data available at: http://www-users.york.ac.uk/~kdc3/papers/coverage2013/had4_krig_v2_0_0.txt
Climate model data used for Figs. 2-4: Models_fullforced_Past1000_GMST_AprMAr.RData: R Workspace containing the model data as time series over the April-March seasonal window, to be consistent with the reconstructions. colnames(models.ama.fullforced) identifies the name of each simulation. Details see Methods section. Models_ctrl_GMST_AprMar.RData: Model control runs. Same format as model data described above.
Forcing data: forcing2.csv: Global mean forcing datasets, semicolon separated. Columns: Year CE, CO2, Volcanic, Solar README_forcing.md: Further information about the forcing datasets (sources etc).
Further results used to generate the Figures: ebm_results_volc_vs_all_forcing300.RData: Results from the Energy Balance Model for Fig. 4a DandA_CESM_ens_30-200.RData: Results from the D&A analysis for Fig. 3 recons.ARnoise.RData: Noise-proxy reconstructions for Figure 4b
Figs_gmst.R: R script to generate the Figures R-functions_gmst.R Functions required to run Figs_gmst.R
Global mean surface temperature reconstructions. Each file is the 1000 member ensemble from one method.File names are methods abbreviations as in the paper; tab separated, members in columns, first column years CE.
Global mean surface temperature reconstructions. Each file is the 1000 member ensemble from one method.File names are methods abbreviations as in the paper; tab separated, members in columns, first column years CE.
Abstract The climate of the Pacific Ocean varies on interannual, decadal, and longer timescales. This variability is dominated by the El Niño–Southern Oscillation (ENSO) and the Interdecadal Pacific Oscillation (IPO), both of which have profound impacts on countries within and well beyond the Pacific. To date, previous studies have only examined a small subset of the possible links between ENSO, its diversity, and the IPO. Here we focus on the statistical relationship between decadal variability in ENSO properties and the IPO, testing the null hypothesis that the IPO arises from random decadal changes in ENSO activity, including ENSO diversity. We use observed sea surface temperature (SST) records since 1920 to investigate how the timing, structure, frequency, duration, and magnitude of El Niño and La Niña events differ between IPO phases. We find that using the relative frequency of El Niño and La Niña events and either the mean event duration or SST magnitude can reproduce up to 60% of the IPO Tripole Index timeseries. While the spatial SST patterns that represent the IPO and ENSO are similar, the IPO is meridionally broader in the central to eastern Pacific, which may be caused by a lagged relationship with low-frequency SST variability in the equatorial Pacific. In addition, North Pacific SST anomalies of opposite sign to the tropical Pacific SST anomalies is a unique feature of the IPO that cannot be explained by decadal ENSO variability. This suggests a clear IPO and ENSO relationship, but also independence in some of the IPO’s characteristics.
The climate of the past two thousand years (2k) provides context for current and future changes, and as such is vital for developing our understanding of the modern climate system. Building on previous phases of the PAGES 2k network, Phase 4 of the PAGES 2k Network paves the way for a new level of understanding of the global water cycle, including enhanced science-policy integration. Previous PAGES 2k network phases emphasised temperature reconstructions, fundamentally improving our understanding of global climate changes over the Common Era. These reconstructions received widespread recognition and were featured in the Summary for Policymakers of the IPCC’s Sixth Assessment Report. Integration of this data with state-of-the-art Earth systems models, proxy system models and data assimilation yielded a more comprehensive understanding of the associated physical drivers and climate dynamics.  Phase 4 challenges our community to turn its focus towards hydroclimate. Our aim is to reconstruct hydroclimatic variability over the Common Era, from local to global spatial scales, at sub-annual to multi-centennial time scales, developing a process-level understanding of past hydroclimate events and variability. Our multi-faceted approach includes (1) developing new hydroclimate syntheses that are well-suited for data-model comparisons, (2) improving the interoperability and scope of existing data and model products, and (3) facilitating the translation of our science into evidence-based policy outcomes. In this presentation, we report on our activities and progress to date, particularly highlighting the early stages of our data synthesis efforts.
Input data for the reconstructions and required datasets and code to generate the figures in the paper. Proxy data used for the reconstructions: proxy_ama_2.0.0_PAGES-crit-regional+FDR.txt: PAGES2k v2.0.0 proxy records, R-FDR screened subset (See PAGES2k Consortium, 2017, Scientific Data, doi: 10.1038/sdata.2017.88). Data are tab separated, the first column is the "paleoData_TSid" to identify each record in the metadata file. First row is year CE. metadata_2.0.0_PAGES-crit-regional+FDR.txt: According metadatam tab separated. Contains a selection of PAGES2k v.2.0.0 database fields in each row: dataSetName, geo_latitude, geo_longitude, archiveType, resMed (Median resolution in years), paleoData_TSid (the column header in the data files). proxy_ama_2.0.0_HR-0.67_infilled_DINEOF_1850-2000_PAGES-crit-regional+FDR.txt: high-resolution (annual and higher) and infilled (calibration period) subset of the proxy data used for the PCR and CPS methods (210 records), see Methods section.
Instrumental target: had4_krig_ama_v2_0_0.txt: April to March aggregated target used for the reconstructions. Space separated, no column headers. Newest updated data available at: http://www-users.york.ac.uk/~kdc3/papers/coverage2013/had4_krig_v2_0_0.txt
Climate model data used for Figs. 2-4: Models_fullforced_Past1000_GMST_AprMAr.RData: R Workspace containing the model data as time series over the April-March seasonal window, to be consistent with the reconstructions. colnames(models.ama.fullforced) identifies the name of each simulation. Details see Methods section. Models_ctrl_GMST_AprMar.RData: Model control runs. Same format as model data described above.
Forcing data: forcing2.csv: Global mean forcing datasets, semicolon separated. Columns: Year CE, CO2, Volcanic, Solar README_forcing.md: Further information about the forcing datasets (sources etc).
Further results used to generate the Figures: ebm_results_volc_vs_all_forcing300.RData: Results from the Energy Balance Model for Fig. 4a DandA_CESM_ens_30-200.RData: Results from the D&A analysis for Fig. 3 recons.ARnoise.RData: Noise-proxy reconstructions for Figure 4b
Figs_gmst.R: R script to generate the Figures R-functions_gmst.R Functions required to run Figs_gmst.R
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