@article{fdi:010087449,
  title = {{L}arge ensemble particle filter for spatial climate reconstructions using a linear inverse model},
  author = {{J}ebri, {B}. and {K}hodri, {M}yriam},
  editor = {},
  language = {{ENG}},
  abstract = {{P}roxy records that document the last 2000 years of climate provide evidence for the wide range of the natural climate variability from inter-annual to secular timescales not captured by the short window of recent direct observations. {A}ssessing climate models ability to reproduce such natural variations is crucial to understand climate sensitivity and impacts of future climate change. {P}aleoclimate data assimilation ({PDA}) offers a powerful way to extend the short instrumental period by optimally combining the physics described by {G}eneral {C}irculation {C}limate {M}odels ({GCM}s) with information from available proxy records while taking into account their uncertainties. {H}ere we present a new {PDA} approach based on a sequential importance resampling ({SIR}) {P}article filter ({PF}) that uses {L}inear {I}nverse {M}odeling ({LIM}) as an emulator of several {CMIP}-class {GCM}s. {W}e examine in a perfect-model framework the skill of the various {LIM}s to forecast the dynamics of the surface temperatures and provide spatial field reconstructions over the last millennium in a {SIR} {PF}. {O}ur results show that the {LIM}s allow for skillful ensemble forecasts at 1-year lead-time based on {GCM}s dynamical knowledge with best prediction in the tropics and the {N}orth {A}tlantic. {T}he {PDA} further provides a set of physically consistent spatial fields allowing robust uncertainty quantification related to climate models biases and proxy spatial sampling. {O}ur results indicate that the {LIM} yields dynamical memory improving climate variability reconstructions and support the use of the {LIM} as a {GCM}-emulator in real reconstruction to propagate large ensembles of particles at low cost in {SIR} {PF}.},
  keywords = {proxy data assimilation ; particle filter ; large ensemble ; spatial ; climate fields reconstructions ; {CMIP}-class climate model emulators},
  booktitle = {},
  journal = {{J}ournal of {A}dvances in {M}odeling {E}arth {S}ystems},
  volume = {15},
  numero = {3},
  pages = {e2022{MS}003094 [26 p.]},
  year = {2023},
  DOI = {10.1029/2022ms003094},
  URL = {https://www.documentation.ird.fr/hor/fdi:010087449},
}